dataesr/scientific-paragraphs-categorization huggingface dataset:
We only retained explanatory factors that accounted for a significant portion of the variability using the "ordistep" function implemented in the vegan R package  (95). Two partial db-RDAs and analysis of variance (ANOVA; 1000 permutations) were conducted to 1) validate whether each factor separately had a significant impact on the variance (in controlling for the second factor) and 2) estimate the variability explained by each factor ("RsquareAdj" function). The effect of a given factor was considered significant when p<0. 05.
All measurements were made in duplicate, and all analyses were based on the threshold cycle (Ct) values of the PCR products. Ct values were averaged across duplicate samples when the variation was not exceeding 1; otherwise, samples were re-run until delta Ct<1. Similarity in relative clade abundance was assessed using PCA analysis of a Bray-Curtis similarity matrix using Hellingertransformed data. Db-RDAs and analysis of variance (ANOVA; 1000 permutations) were conducted to identify whether either temperature or time had a significant impact on Symbiodinium assemblage, and an alpha level of 0. 05 was set a priori. To complement data from the experimental individuals, qPCRs were carried out on mantle fragments from 10 wild individuals and seawater samples collected in Reao Atoll (Tuamotu, corresponding to the geographical origin of the experimental individuals; see  Brahmi et al.
(43)  for details. ) in October 2018. Preparation and analyses were performed as described above and in Rouzé et al. (62). As a more detailed means of assessing Symbiodinium diversity in the 24 small giant clam samples, a meta-barcoding analysis was undertaken following the protocol of  (97). Briefly, the ITS2 gene was PCR amplified using previously described primers set  (97)  and sequenced at McGill University's "Genome Quebec Innovation Center" (Montréal, QC, Canada) on a Illumina Miseq 250 bp paired-end platform. The Dada2 algorithm  (98) , implemented in QIIME2 software  (99) , was used to infer sample exact sequences from amplicon data. The reference database was directly imported from NCBI nt repository and trained on the basis of the ITS2 primers following     S1 : Transcriptome annotations for Symbiodinium spp.
and Tridacna maxima. Transcripts were aligned against the Uniprot-Swissprot database with BLASTx, and only the besthit results have been shown. Table  S2 : Excel file featuring gene ontology (GO) enrichment for each module. Only GO terms with p<0. 05 and at least three genes were considered to be significantly enriched. Table  S3 : Table  S3. Excel file featuring "hub" genes detected within each module. The top 30 hub genes were selected according to their module membership values (kME). red  (89)  turquoise  (10, 061) • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • •• • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • 0.
(39)ing et al. (97)recommendations. Detailed protocol, including scripts, has been made available in a Github public repository (https://github. com/jleluyer/acclimabest). data. HAM, CB, ABM, and JLL wrote the manuscript. All co-authors contributed substantially to revised drafts of the manuscript. Competing interests:We declare that we have no competing interests. Data and materials availability: Raw sequencing RNA-Seq data for small giant clams featured herein have been made publicly available on the NCBI database (pending creation), and all scripts discussed in the article can be found on Github (https://github. com/jleluyer/acclimabest). Raw meta-barcoding data are available here: (pending creation). Data for free-living Symbiodinium spp. have been previously deposited on the NCBI database: Levin et al.
(37)-BioProject NCBI: PRJNA295075, Gierz et al. (36)-BioProject NCBI: PRJNA342240. Data for Symbiodinium spp. from the reef coral P. damicornis(39)can be found on the NCBI database (Sequence Read Archive: SRR1030692 and BioProject: PRJNA227785), as well as on this modular, interactive website: http://symbiont. iis. sinica. edu. tw/coral_pdltte/static/html/index. html#home.
Acknowledgments General: We are grateful to  Michel Pahuatini  for providing the small giant clams and seawater samples. We thank  Claude Soyez  and  Leila Chapron  for their help in animals rearing. We thank  Dr. Laetitia Hédouin , for valuable comments on prior versions of the manuscript. We also would like to acknowledge  Corinne Belliard  for help in DNA preparation and  Dr. Yu-Bin Wang  for making the P. damicornis-Symbiodinium transcriptomic resources openly accessible, as well as designing the afore-cited P. damicornis-Symbiodinium transcriptome server. Finally, we thank  O. Bichet  for her help with the figures. Funding: This experiment was made possible by a grant from  Labex CORAIL (France) , as well as an  IFREMER  grant (France; Master's internship to HAM). Author contributions: CB and JVD conceived the  GECO  project, under which tissue samples featured herein were collected.
Parallel to the monthly sampling of cells for cultivation, dissolved nutrients, benthic coverage, and coral health  (Smith et al. , 2016)  were assessed at the same sampling sites. Additionally, two buoys (Mooring VI104, CariCOOS Data Buoy C and Station 41052, National Buoy Data Center) provided hourly hydrological data from nearby locations (Supplementary Table  S1 ). To obtain the individuals needed for the genetic analysis, single Gambierdiscus cells were isolated from the epiphyte collections. From each sampling site and date, at least 10 Gambierdiscus cells were isolated by micropipetting under an inverted microscope (Olympus CK40). Each cell was washed by serial transfers through sterile seawater, and transferred individually into a well of a 96-well plate containing 300 µl of K medium  (Keller et al.
Analysis of Microsatellite Data Microsatellite peaks were scored in Geneious 8. 1. 7 (Kearse et al. , 2012)  based on a LIZ 600 size standard. Allele frequencies, genetic diversity, and fixation indices of genetic differences (F ST ) between populations were calculated using the software GenoDive 2. 0b27  (Meirmans and Van Tienderen, 2004). F ST values and their significance based on 9999 permutations were assessed for three different datasets: all seven loci, only the three loci from the transcriptome and only the five loci that did not show any sudden changes in allele frequencies. Allele diversity (based on all seven markers) was calculated for individual temporal groups including all microalgal strains as well as only unique ones, meaning that reoccurring genotypes were excluded. To assess genotype diversity, clones were assigned based on a step-wise mutation model in GenoDive.
For this analysis, missing data were replaced by average allele length and a threshold of two bases was chosen to distinguish clones. Genotype diversity was also calculated after equalizing the sample size of each genetic cluster to N = 54 with the R function "sample. " Pairwise Jost's D values between genetic clusters were calculated in the R package DEMEtics 0. 8-7  (Gerlach et al. , 2010)  and significance of each value was assessed with 1000 bootstrap resamplings. Genotypic linkage disequilibrium between pairs of loci was calculated with the web version of GenePop  (Rousset, 2008)  using 1000 dememorizations, 100 batches, and 1000 iterations per batch. Clustering of algal strains, roughly grouped by month, was determined via principal component analysis (PCA), which was calculated in Genodive and visualized in R.
The significance of the axes was assessed using 1000 permutations. The number of distinct genetic clusters was determined by the program STRUCTURE, version 2. 3. 2  (Pritchard et al. , 2000). Default parameters as well as prior information about sampling time points, admixture and non-admixture ancestry models with either correlated or independent allele frequencies were tested in STRUCTURE. Each run had a burn-in of 70000 iterations followed by 200000 iterations of data collection. Up to 12 populations, generously exceeding the number of identified clusters by PCA, were tested with 10 iterations at each level. The results were analyzed using the web based program STRUCTURE Harvester  (Earl and vonHoldt, 2011)  to identify the number of clusters yielding the best fit for this dataset. Genetic clustering in the high-resolution sampling period at USVI (1308-1604) based on the STRUCTURE results as well as other putative groupings was further investigated with an AMOVA using the program Genodive.
The relationship between genetic structure and environmental variables was assessed using a distance based redundancy analysis (dbRDA)  (Legendre and Anderson, 1999). Distances between approximate monthly algal groups based on the microsatellite data were calculated with a principal coordinate analysis (PCoA) using the R package adegenet 1. 0. 1  (Jombart, 2008). The relationship between the coordinates of algal groups (row scores normed to the square root of the eigenvalue = li) from the PCoA and the environmental variables was tested with dbRDA using the R package vegan 2. 4-1  (Oksanen et al. , 2016). The best model was selected based on automatic stepwise model building using the function "ordistep" in the R package vegan starting with a model that included only the intercept, and adding all environmental variables afterward.
) There were 293 unique seven-locus genotypes in the entire data set resulting in an overall genotype diversity of 0. 72 (G/N). No linkage disequilibrium was detected across the dataset among all pairs of loci. To detect distinct genetic clusters in the data set, we used Bayesian analysis implemented in the program STRUCTURE. This program gave the most biologically sensible results using prior sampling information, as this reduces the bias due to uneven sample sizes  (Falush et al. , 2003) , admixed ancestry and independent allele frequency settings. Using Ln(K) values and the Evanno method, K = 4 was identified as the most likely number of genetic clusters in the entire data set (Figure  2A ). The STRUCTURE results indicated succession of four different genetic clusters at USVI, while FlK_13 and FlK_14 were very similar to USVI_06, USVI_07, and USVI_08.
Impact of Environmental Variables on Population Structure The impact of several biotic and abiotic environmental variables, which changed throughout the sampling period, on the population genetic structure of G. caribaeus at USVI was assessed using a distance based redundancy analysis (dbRDA). The dbRDA analysis identified significant relationships between the microsatellite data and salinity, percent habitat covered by corals, turfalgae growing on dead corals and cover of the two macroalgal species Lobophora variegata and Halimeda spp. (Figure  5  and Table  5 ). Comparing the means of all environmental variables from the three time periods at USVI dominated by distinct genetic clusters of G. caribaeus with one-way ANOVA revealed further significant differences that characterized each phase.
ACKNOWLEDGMENTS We thank  Mindy Richlen  for providing cell count data of Gambierdiscus from  USVI. Alex Leynse  and  Adam Catasus  contributed to this study by establishing Gambierdiscus cultures from the  Florida Keys. Field work in St. Thomas was facilitated by  Robert Brewer ,  Viktor Brandtneris ,  Rosmin Ennis ,  Sarah Groves ,  Sarah Heidmann ,  Colin Howe ,  Jonathan Jossart ,  Elizabeth Kadison ,  Christopher Loeffler , and  Tanya Ramseyer. We are grateful for constructive comments from two reviewers that improved the manuscript.
Introduction Organic walled dinoflagellate cysts are extremely useful for stratigraphic correlation and for reconstructing palaeoenvironments. For example, they facilitate the determination of hydrological parameters such as sea-surface temperature, salinity, primary productivity, nutrient content, turbidity/stratification of the water column, seasonal sea ice cover and bottom water ventilation (e. g. Dale 1976; Wall et al. 1977; Turon 1984; de Vernal et al. 1994 ; and see  Zonneveld et al. 2013  for further references). Databases that include the modern geographic distribution of dinoflagellate cyst species are essential for such determinations. During the last four decades, development of such databases has revealed the need for consistency in species identification. Otherwise, the data are compromised.
Acknowledgements This compilation was made possible thanks to the very fruitful discussions we had during informal exchanges or round tables with the numerous colleagues who came to the workshops held in Montreal and Ostend/Ghent. The opportunity at these workshops to see the holotypes or specimens from reference material brought by several colleagues has been of priceless value to us in developing the Identification Key. We thank  Kazumi Matsuoka  for kindly allowing us to re-photograph the holotypes he brought in Ghent and reproduce these images in this paper. We would like to especially thank the initiators and organizers of the two workshops, especially  Anne de Vernal  ( UQAM/Geotop, Montreal, Canada ) and  Kenneth Mertens  ( Ifremer, Concarneau, France ). We are indebted to  Shan Xu  ( Bordeaux Univ.
Acknowledgments The authors would like to acknowledge support from the  Swedish Government through Sida, The Swedish International Development Cooperation Agency. We also acknowledge logistical support from the  CORDEX International  Project Office, the  Swedish Meteorological Institute  and the Climate System Analysis Group at the  University of Cape Town. We also acknowledge the  World Climate Research Programme 's Working Group on Coupled Modelling, which is responsible for CMIP, and we thank the climate modelling groups for producing and making available their model output.
1998 ) and the NeMys database (http://www. marinespecies. org). Species description was made from glycerol slides using interferential contrast microscopy, and drawings were made with a camera lucida. Images of the specimens were taken with an Axio Apotomedigital camera and processed with ZEN imaging software. All measurements are in µm and all curved structures were measured along the arc. Slides with the type specimens of the new species are deposited in the collection of the Muséum national d'Histoire naturelle, Paris (MNHN). Abbreviations used:     Measurements. See Table  I  Female. Body length can exceed that of the male (maximum body length 2370 µm). Two reflexed  ovaries; the anterior one right and the posterior one left of the gut. Vulva about 8 µm long and weakly cuticularised. This species is assigned to the genus Chromadorita because of the homogeneous cuticle, without lateral differentiation, and the larger hollow dorsal tooth.
Acknowledgements We are indebted to the Chief Scientists of the cruises that allowed the sampling of the specimens used in this study - M. Sibuet  and  A. Khripounoff , Ifremer, Brest (BIOZAÏRE 2 & 3 Cruises)as well as to the crew of the  R/V L'Atalante  and the team of the submarine ROV Victor 6000. Both cruises were arranged thanks to the multidisciplinary programme BIOZAÏRE, a partnership between Ifremer and  Total Oil Company. The authors would also like to thank  K. Olu  for her useful suggestions on the manuscript. EB and DZ were partially supported by the project " Prokaryote-nematode Interaction in marine extreme envirONments: a uniquE source for ExploRation of innovative biomedical applications " (PIONEER) funded by the  Total Foundation and Ifremer.
3-11. 4 cal ka BP (2σ interval) and suggests glacier retreat from this area at the beginning of the Holocene. On the northern slopes of the central Cantabrian Mountains, the sequence of recessional moraines dated in the nearby Monasterio valley (BRA-VAL samples in the north slope of the range) yield minimum 10 Be CRE ages in the range 18. 1-16. 7 ka that are equally consistent with glacier retreat after the LGM of MIS 2  (Rodríguez-Rodríguez et al. , 2017). Based on the preexisting datasets, we have performed new 10 Be CRE analyses on twenty samples taken from lateral and recessional moraines along the Porma valley and its main tributaries to check our hypothesis that glaciers remained close to their maximum extent position during the LGM of MIS 2. Additionally, we report sixteen radiocarbon and seven OSL ages obtained on glacial, glacial-related and post-glacial deposits to cross-check if timing asynchronies exist between the results of the different dating techniques and to discuss eventually their possible causes.
An amount of 14-18 g of pure quartz was spiked with ~0. 1 g of Beryllium standard solution and digested in hydrofluoric acid. The Beryllium standard is an in-house solution (3025 ± 9 ppm 9 Be) prepared from a phenakite crystal mined at great underground depth  (Merchel, 2008 , Braucher et al. , 2015). Beryllium extraction was done using column chromatography and precipitation by neutralization. Beryllium hydroxides were dried after rinsing with pH 8 MQ water in order to remove isobar boron, and finally oxidized at 800ºC to BeO in porcelain crucibles. The isotopic ratio 10 Be/ 9 Be of each sample was measured at the French 5 MV AMS facility ASTER (Aix-en-Provence)  (Arnold et al. , 2010 , Klein et al. , 2008). Data were calibrated against three targets of reference material SRM4325 using an assigned 10 Be/ 9 Be ratio of (2.
79 ± 0. 03) x 10 -11  (Nishiizumi et al. , 2007)  and a 10 Be half-life of (1. 36 ± 0. 07) x 10 6  (Korschinek et al. , 2010 , Chmeleff et al. , 2010). Reported analytical uncertainty includes external uncertainty of 0. 5%, which accounts for all effects contributing to ASTER's variability and is based on long-term standard measurements  (Arnold et al. , 2010) ; counting statistics uncertainty of ca. 3% (~1000 events) and chemical blank correction uncertainty. The isotopic ratios measured for each sample were converted to 10 Be concentration in the quartz sample  (Balco, 2006)  and used to calculate exposure ages with the online exposure age calculator formerly known as the CRONUS-Earth 10 Be exposure age calculator  (Balco et al. , 2008)  using version 2. 3 (released in June 2016) and considering the global production rate based on the primary calibration dataset in  Borchers et al.
Similarities and discrepancies between dating results relying on the different techniques The new 10 Be CRE chronology presented in this work is generally time-consistent with previous 10 Be CRE ages reported for the glacial sequence in the Porma valley  (Rodríguez-Rodríguez et al. , 2016). The minimum abandonment ages obtained in the Runción (20. 2 ± 0. 9 ka), Robledo analyses of large global datasets, partial exposure due to post-depositional shielding occurs more often than over-exposure with respect to the deposition time due to prior exposition  (Heyman et al. , 2011). In the present study, boulder ages that are more than two standard deviations older than the rest of the group mean age are considered too old and excluded from the average moraine age calculation (time of moraine abandonment).
, 2010)and the OSL age of supraglacial till sediments from Vega del Naranco terminal moraine at Fuentes Carrionas Massif (~36 ka;Serrano et al. , 2013)are equally consistent with the glacial record documented here for the central Cantabrian Mountains. The minimum exposure ages of the RUN and ROB lateral moraines suggest that the last deglaciation started at c. 21-20 ka in the central Cantabrian Mountains, consistently with the evolution of the Sanabria Lake record(Rodríguez-Rodríguez et al. , 2014). The 10 Be CRE age dataset of the Monasterio valley indicates a consistent deglaciation sequence with recessional stages at minimum ages of c. 18. 1 ka; 16. 7 ka; and 14 ka(Rodríguez-Rodríguez et al. , 2017).
cCREagescalculated with the latest version of the online exposure age calculator formerly known as the CRONUS-Earth online exposure age calculator v2. 3 (http://hess. ess. washington. edu/math/;November2016;Balcoetal. , 2008)considering the default primary calibration dataset ofBorchers et al. (2016)and the Lm scaling scheme. No snow and no erosion corrections were applied. d.
Cantabrian Mountains deduced from the Porma and Monasterio datasets (Rodríguez-Rodríguez et al. , 2017) sequences provide a complete picture of the Last Glaciation in the central Cantabrian Mountains in northern Spain. Although the terminal moraines are not preserved in the Porma catchment, the remaining glacial evidence suggests that the first glacial stage (Stage I, Fig. 9) of the Last Glacial Cycle took place early (~ 110 ka) coevally with MIS 5d. Some boulders provide even oldest ages (~170-150 ka) that might be inherited from a previous glaciation during MIS6 (Rodríguez-Rodríguez et al. , 2016). In contrast, evidence of these old glacial advances has not been found in the northern slope yet, probably because the topography is generally steeper and the valleys are narrower, conditions that did not favor the preservation of glacial evidence.
Jalut et al. , 2010)and the OSL age of supraglacial till sediments from Vega del Naranco terminal moraine at Fuentes Carrionas Massif (~36 ka;Serrano et al. , 2013)are equally consistent with the glacial record documented here for the central Cantabrian Mountains. The minimum exposure ages of the RUN and ROB lateral moraines suggest that the last deglaciation started at c. 21-20 ka in the central Cantabrian Mountains, consistently with the evolution of the Sanabria Lake record(Rodríguez-Rodríguez et al. , 2014). The 10 Be CRE age dataset of the Monasterio valley indicates a consistent deglaciation sequence with recessional stages at minimum ages of c. 18. 1 ka; 16. 7 ka; and 14 ka(Rodríguez-Rodríguez et al. , 2017). The Porma glacier margins remained in positions close to the previous stage during MIS 3 and MIS 2 (~56-22 ka; Stages IIa to IIb), as indicate the presence of diachronous boulders (like RES-04) in various lateral and medial moraines (e.
1-31. 5 cal ka BP) is consistent with minimum deglaciation sequences (Fig. 9). Regarding the timing of rock glacier activity (periglaciation), available ages obtained in the CEL erratic boulders and suggest glacier free conditions for the Celorno 10 Be CRE chronologies for two rock glaciers suggest the stabilization of the foremost ridge valley by the time of the LGM culmination (Stage IIb; Fig. 9). In the northern slope of the during the time interval ~15. 7-13 ka (Rodríguez-Rodríguez et al. , 2017, Rodríguez- range, Stage IIb is recorded by the minimum radiocarbon age of ~33. 5 cal ka BP obtained Rodríguez et al. , 2016). The available deglaciation datasets show apparently longer time close to the base of the Brañagallones kame terrace sequence, outside the outermost lateral resilience of glaciers in the northern slope of the range compared to the southern slope, moraine (Jiménez-Sánchez and Farias-Arquer, 2002), while a burial OSL age of c.
Data analyses and statistics All data analyses were performed using R Studio (R Core Team, 2016). All figures were prepared using the ggplot2 package  (Wickham, 2009) , with the exception of the contour plots presented in Fig. 2 , which were prepared using Ocean Data View 4. 7. 10  (Schlitzer, 2017). Chlorophyll fluorescence of microbial groups was calculated as the ratio of mean red fluorescence of each cell population to that of the reference beads, based on flow cytometry results. Per-cell biomass was calculated using previously published conversion factors: 29 fg C per Prochlorococcus cell, 100 fg C per Synechococcus cell, 11. 5 fg C per bacte- rial cell, and 1500 fg C per PPE cell  (Zubkov et al. , 2000). Biomass contributions of different phytoplankton groups were estimated by multiplying cell abundances by the conversion factors above and integrating by depth across the euphotic zone.