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Navigation and meteorological data collected during the Tara Pacific Expedition 2016-2019
<p>The Tara Pacific expedition (2016-2018) sampled coral ecosystems around 32 islands in the Pacific Ocean, and sampled the surface of oceanic waters at 249 locations, resulting in the collection of nearly 58,000 samples. The expedition was designed to systematically study corals, fish, plankton, and seawater, and included the collection of samples for advanced biogeochemical, molecular, and imaging analysis. Here we provide the continuous dataset originating from navigation and meteorological instruments acquiring continuously during the full course of the campaign.</p> <p> </p> <p>Variables/ descriptions and units:</p> <table> <tbody> <tr> <td>variable</td> <td>description</td> <td>units</td> </tr> <tr> <td>'dt'</td> <td>date-time stamp</td> <td>iso UTC</td> </tr> <tr> <td>'lat'</td> <td>latitude</td> <td>decimal degree</td> </tr> <tr> <td>'lon'</td> <td>longitude</td> <td>decimal degree</td> </tr> <tr> <td>'flag_origin_latlon'</td> <td>origin of the latitude and longitude</td> </tr> <tr> <td>'cog'</td> <td>course over ground</td> <td>degree</td> </tr> <tr> <td>'sog'</td> <td>speed over ground</td> <td>knots</td> </tr> <tr> <td>'sst_batos'</td> <td>Sea surface temperature measured by the navigation station</td> <td>°C</td> </tr> <tr> <td>'temperature_atm'</td> <td>Atmospheric temperature</td> <td>°C</td> </tr> <tr> <td>'pressure_sealevel'</td> <td>Atmospheric presure</td> <td>hp</td> </tr> <tr> <td>'relative_humidity'</td> <td>relative humidity </td> <td>%</td> </tr> <tr> <td>'apparent_windspeed_bow'</td> <td>apparent wind speed</td> <td>knots</td> </tr> <tr> <td>'apparent_winddir_bow'</td> <td>wind direction from the bow</td> <td>degree</td> </tr> <tr> <td>'apparent_wind_trueN'</td> <td>wind direction from north</td> <td>degree</td> </tr> <tr> <td>'true_wind_speed'</td> <td>knots</td> </tr> <tr> <td>'true_wind_dir'</td> <td>wind direction from north</td> <td>degree</td> </tr> <tr> <td>'sunzenith'</td> <td>sun position relative to zenith</td> <td>radian</td> </tr> <tr> <td>'sunazimuth'</td> <td>sun position relative to north</td> <td>radian</td> </tr> </tbody> </table>
Chlorophyll a concentration, particulate organique carbon, and particle mean size index [gamma; 0.2 - 20 µm] measured using an hyperspectral spectrophotometer [ACS, Wetlabs] during the Tara Pacific Expedition 2016-2018
<p>The Tara Pacific expedition (2016-2018) sampled coral ecosystems around 32 islands in the Pacific Ocean, and sampled the surface of oceanic waters at 249 locations, resulting in the collection of nearly 58,000 samples (Gorsky et al. 2019, Planes et al. 2019, Flores et al. 2020). The expedition was designed to systematically study corals, fish, plankton, and seawater, and included the collection of samples for advanced biogeochemical, molecular, and imaging analysis. Here we provide the continuous dataset originating from the hyperspectral and multispectral spectrophotometers [ACS] instruments acquiring continuously during the full course of the campaign. Surface seawater was pumped continuously through a hull inlet located 1.5 m under the waterline using a membrane pump (10 LPM; Shurflo), circulated through a vortex debubbler, a flow meter, and distributed to a number of flow-through instruments. An [ACS] spectrophotometer (WETLabs) measured hyper-spectral (4 nm resolution) attenuation and absorption in the visible and near infrared except between Panama and Tahiti where an AC-9 multispectral spectrophotometer (WETLabs) was used instead. The flow was automatically directed through a 0.2 µm filter for 10 minutes every hour before being circulated through the spectrophotometer to eliminate the impact of biofouling and instrument drift and estimate particulate absorption [ap] and attenuation [cp] (Slade et al. 2010). Chlorophyll a content was estimated from particulate absorption line height at 676 nm (Boss et al. 2001). The particulate organic carbon concentration [poc] was estimated using an empirical relation (Gardner et al. 2006) between measured [poc] and measured [cp]. An indicator for size distribution of particles between 0.2 and ~20 µm [gamma] was calculated from [cp] (Boss et al 2001). The data was processed with custom software for underway optical data (InLineAnalysis software available on GitHub). The detailed information regarding the data processing is given in the processing report attached with the data and in Lombard et al. (In prep.). These results are preliminary: no matchup with in-situ chlorophyll from HPLC or [poc] measurements were performed.</p>
Historical Sea Surface Temperature (SST) data and thermal stress indices of the Tara Pacific Expedition's coral reef sampling sites, from May 1st 2002 to August 31st 2018.
<p>The Tara Pacific expedition (2016-2018) sampled coral ecosystems at 111 sampling sites around 32 islands in the Pacific Ocean, and sampled the surface of oceanic waters at 249 locations, resulting in the collection of nearly 58,000 samples (Gorsky et al. 2019, Planes et al. 2019, Flores et al. 2020). The expedition was designed to systematically study corals, fish, plankton, and seawater, and included the collection of samples for advanced biogeochemical, molecular, and imaging analysis.</p> <p>Here we provide a high-resolution historical dataset that spans from 2002 to each sites’ sampling date and gives an overview of past climate variability and heatwaves experienced by corals sampled at each site. Ocean skin temperature (11 and 12 µm spectral bands longwave algorithm) was extracted from 1km resolution level-2 MODIS-Aqua and MODIS-Terra from 2002 to the sampling date and from level-2 VIIRS-SNPP from 2012 to the sampling date. Day and night overpasses were used to maximize data recovery. Following recommendations from NASA Ocean Color (OB.DAAC), only SST products of quality 0 and 1 were used. The 9 closest pixels to the sampling sites of each scene were extracted. All the extracted pixels from the 3 satellites were then averaged daily to obtain daily SST averages and standard deviations time series for each sampling site, from 2002 to the sampling date.</p> <p>Each time series was first averaged on a Julian day basis to provide a seasonal average. This yearly seasonal average was triplicated and concatenated into a 3-year seasonal cycle to apply a digital low pass filter on the middle year without generating artifacts. A digital low pass filter (filter order 3, pass band ripple 0.1; “filfilt” function in matlab) with 36 Julian days windows was applied to the concatenated time series to remove high frequency noise. The middle year was then extracted from the concatenated time series to recover the seasonal cycle. The sea surface temperature anomaly was calculated as the SST minus the seasonal cycle over the full time series. Considering the short periods of missing data (mean of the 95th percentile of the duration of consecutive days with missing data: 9.8 ± 4.1 days), the missing values in the SST and SST anomaly time series were linearly interpolated in order to calculate thermal stress indices. The SST anomaly frequency was calculated as the number of days over the past 52 weeks when the SST anomaly is greater than or equal to 1 °C. Thermal stress indices relevant to coral reef health were then calculated using methodology developed for the Coral Reef Temperature Anomaly Database (CoRTAD) data base (Saha et al. 2019). Events of cold temperature accumulation were also reported to cause bleaching and mortality (Lirman et al. 2011; González-Espinosa & Donner 2020), therefore, the same set of indices were calculated for cold stress adapting the CoRTAD method, but using the minimum weekly climatologies.</p> <p>A condensed table containing single values associated with each sampling site was created ('TaraPacific_SST_timeseries_mean_products') extracting the minimum, maximum, sum, averages, standard deviations, and value recorded at the sampling day of each of these indices (detailed in the readme file provided with the dataset 'README_TaraPacific_historical_SST.md'). Additional metrics of the last heating and cooling events as well as the time of recovery is also provided to represent the state of thermal stress at the day of sampling.</p>
Eulerian and Lagrangian diagnostics of the dynamical properties of the water masses sampled during the Tara Pacific Expedition 2016-2018
<p>In order to provide a description of the dynamical properties of the water masses sampled, different Eulerian and Lagrangian diagnostics were calculated. </p> <p>For each of the 246 stations sampled, we proceeded as follows.</p> <p>We identified the water mass sampled at the given station. This was considered as a stadium shape with the two semi-circles centered on the starting and ending points of the transect, respectively. The radius of the stadium semi-circles was considered 0.1°, which is in accordance with previous studies25,29,30. The stadium was filled with virtual particles separated by 0.01°.</p> <p>For each virtual particle inside the stadium shape, we calculated an Eulerian or Lagrangian diagnostic (described above). The Eulerian diagnostics were extracted directly from the velocity field of the day of sampling. Concerning the Lagrangian diagnostics, these were obtained by advecting the virtual particle backward in time for an amount of time 𝞽 from the day of sampling day_S. For the Lagrangian betweenness, the advection was performed between day_S+𝞽/2 and day_S-𝞽/2, so that the advective time window was centered on the sampling day (details in25).</p> <p>For the Lagrangian diagnostics, we used the following advective times 𝞽: 5, 10, 15, 20, 30, and 60 days. The only exception is the retention time, which, by construction, was calculated only with the largest advective time, namely 𝞽=60 days.</p> <p>Once that, a given diagnostic (Eulerian or Lagrangian) was calculated for all the virtual particles filling the stadium shape, we calculated the mean value, and the 25, 50, and 75 percentiles. The percentiles were calculated in order to quantify the spatial variation of the diagnostic inside the stadium shape. Therefore, we associated each station with four values (mean, 25, 50, and 75 percentiles) of a given diagnostic.</p> <p> Furthermore, two different velocity fields were used, which are described as follows. </p> <p>Both the velocity fields were downloaded from E.U. Copernicus Marine Environment Monitoring Service (CMEMS, http://marine.copernicus.eu/). The first velocity field used was MULTIOBS_GLO_PHY_REP_015_004 [GlobEkmanDt]. This was produced by combining the altimetry derived geostrophic velocities and modeled Ekman surface currents. It had a spatial resolution of 0.25° and a temporal resolution of one day. The second velocity field was GLOBAL_REANALYSIS_PHY_001_030 [GloryS12]. It was obtained by a NEMO model assimilating altimetry and other observations. It had a spatial resolution of 1/12° and a temporal resolution of 1 day.</p> <p>The following Eulerian diagnostics were calculated:</p> <ul> <li> <p>Absolute velocity ([Uabs], m s-1): sqrt(u2+v2), where u and v are the zonal and meridional components of the horizontal velocity field used (described below)</p> </li> <li> <p>Kinetic energy ([Ekin], m2 .s-2): 0.5*(u2+v2)</p> </li> <li> <p>Divergence ([EulerDiverg], d-1): du/dx + dv/dy</p> </li> <li> <p>Vorticity ([Vorticity], d-1): dv/dx - du/dy</p> </li> <li> <p>Okubo-Weiss ([OW], d-2): s2-vorticity2, where s2 is (du/dx-dv/dy)2 + (dv/dx+du/dy)2. If negative, it indicates that the station sampled was inside an eddy.</p> </li> </ul> <p>The following Lagrangian diagnostics were calculated:</p> <ul> <li> <p>Finite-Time Lyapunov Exponents ([Ftle], d-1): it indicates the rate of horizontal stirring, and it is a means to quantify the intensity of turbulence in a given region. FTLE are commonly used to identify Lagrangian Coherent Structures, i.e. barriers to transport. In this case, a strong FTLE value indicates a region separating water masses which were far away backward in time.</p> </li> <li> <p>Lagrangian betweenness ([betw], adimensional): this diagnostic draws inspiration from Lagrangian Flow Network Theory26. It can identify regions which act as bottlenecks for the circulation, in that they receive waters coming from different origins, and that are then spread over several different destinations. These can represent possible hotspots driving biodiversity25.</p> </li> <li> <p>Lagrangian Divergence ([LagrDiverg], d-1). This diagnostic was calculated by integrating the Eulerian divergence along the backward trajectories. If positive, it indicates a water mass that, during the previous days, was subjected to a strong divergence, thus to a possible upwelling. If negative, it indicates a strong convergence, thus possible downwelling.</p> </li> <li> <p>Retention Time ([RetentionTime], d). This diagnostic indicates how many days a water mass has spent inside an eddy in the previous period. If the water mass is outside an eddy, then its retention time is set to zero.</p> </li> </ul>
Environmental context observed during the Tara Pacific Expedition 2016-2018, simplified version at site level
<p>This dataset provide a site level-compilation of previous datasets provided at the event level (see https://zenodo.org/record/6445609#.YlP8B5NByEA). In some cases, certain parameters were not available at specific sampling sites due to technical issues or sensor availability, however, various basin scale studies and statistical tests require a complete dataset for all sampled sites. During the Tara Pacific expedition, many parameters were concurrently measured in-situ, estimated from remote sensing and/or modeled. For instance, sea surface temperature was measured on the boat using the thermosalinograph included in the underway system, but also with satellite and estimated from a model. Each of these three modes of acquisition have their caveat and accuracy, however, within a certain confidence interval, missing in-situ data can be replaced by its remotely sensed or modeled equivalent. We provide here a simplified version at the sampling site level by replacing missing in-situ data by their closest and most accurate satellite or modeled equivalent. In each case, in-situ data was considered as the most accurate source of data, with a preference to HPLC pigments analysis followed by measurements done by the ACS, while satellite and modeled data were used only if in-situ data was not available. We evaluated the accuracy of ACS and of each satellite and modeled datasets by linear regressions with their in-situ counterparts. A bias of the modeled or satellite data was identified when the slope of the regression was different to 1 and/or an intercept was different to 0. The satellite and modeled data were forced to match the in-situ data by dividing by the slope and subtracting the intercept. This is the case for SST. When large bias persisted between matchups with observations, the corrected data was not used to replace missing in-situ data. This is the case for chl. The same approach was then applied to fill missing data with modeled values (MERCATOR-Copernicus).</p> <p>A correction for the bias in the following variable was applied for SST, SSS, PO4, and SiOH. As previously done, if large bias persisted between observations and corrected data, they were not used to replace missing in-situ data. This is the case for chl, NO3, and Fe.</p> <p>The [MTE] samples were sometimes sampled in the afternoon instead of the morning alongside all the other water samples, thus were located in between two sampling stations. These [MTE] samples could not be assigned to a sampling station following the criterion presented in the section 3, therefore, the missing values of the corresponding morning stations were interpolated linearly.</p> <p>The same approach was used for pH measurements, with a preference from measurements provided by total carbonate system quantifications, followed by direct pH measurements and then modeled values (MERCATOR-Copernicus).</p>
TARA Pacific CTAX colony morphological annotations release version 1_1
<p>PHOTO dataset (<em>in situ</em> photos) and the colony morphometric analysis using <em>in situ </em>photographs of two scleractinian corals and one hydrozoan coral taken during the TARA Pacific Expedition: <em>Pocillopra </em>spp., <em>Porites </em>spp. and <em>Millepora </em>spp. respectively.</p>
Assemblies of 269 Metagenomic Tara Pacific Sequencing Samples - part 2
<p>This data is the result of the metagenomic assembly of 269 sequencing samples reflecting a first subset of the Tara Pacific metagenomes. Assemblies are used in </p> <p>- Preprint: <a href="https://doi.org/10.1101/2022.04.11.487905">Endogenous viral elements reveal associations between a non-retroviral RNA virus and symbiotic dinoflagellate genomes</a></p> <p>- <a href="https://doi.org/10.5281/zenodo.7839794">Part 1</a></p>
Assemblies of 269 Metagenomic Tara Pacific Sequencing Samples - part 1
<p>This data is the result of the metagenomic assembly of 269 sequencing samples reflecting a first subset of the Tara Pacific metagenomes. Assemblies are used in </p> <p>- Preprint: <a href="https://doi.org/10.1101/2022.04.11.487905">Endogenous viral elements reveal associations between a non-retroviral RNA virus and symbiotic dinoflagellate genomes</a></p> <p>- <a href="https://doi.org/10.5281/zenodo.7840044">Part 2</a></p>
TARA-PACIFIC_telomere-length
<p>Samples : High molecular weigth DNA was extracted from an apex of coral nubbins from the TARA-Pacific CS40 sampling. Telomere length was measured using Telomere Restriction Fragment assay using radioactive probes to measure the host telomere length sequence (TTAGGG)<sub>n</sub>, also referred as T2, or the symbionts telomere length sequence (TTTAGGG)<sub>n</sub>, also referred as T3.</p> <p>Measurements was successfully performed for the host telomere repeat sequence on 1098 samples, for the symbiont repeat sequence on 882 samples, measurements were successfully performed several times with the host telomere probe for 248 samples and with the symbiont telomere probe for 115 samples.</p> <p> </p> <p> </p> <p><strong>File structure</strong></p> <p>Variables :</p> <p><strong>1-sampleID</strong>: TARA PACIFIC Colony ID barcod</p> <p><strong>2- sampling-design:</strong> TARA PACIFIC Colony ID encoding for sampling, the island, site and colony (ex: OA000-I01S01C001 stands for Island 1, site 1 and coral colony 1)</p> <p><strong>3-n: </strong>Number of measurements done with the host telomere probe (TTAGGG)<sup>*</sup>.</p> <p><strong>4- Mean:</strong> Host mean telomere length measured in kilobases.<sup>*</sup></p> <p><strong>5- standard-deviation: </strong>Standard deviation of the mean measured between the number of measurements done with the host telomere probe (TTAGGG)<sup>*</sup>.</p> <p><strong>6-percentile-50:</strong> Host median telomere length measured in kilobases.<sup>*</sup></p> <p><strong>7-percentile-75:</strong> Host 3<sup>rd</sup> quartile telomere length limit below which lies 75% of the signal, measured in kilobases.<sup>*</sup></p> <p><strong>8-percentile-25:</strong> Host 1<sup>st</sup> quartile telomere length limit below which lies 25% of the signal, measured in kilobases.<sup>*</sup></p> <p><strong>9-interpercentile-p75-p25-distance:</strong> Distance between host_percentile-25 and host_percentile-75 telomere length, measured in kilobases.<sup>*</sup></p> <p><strong>10-n: </strong>Number of measurements done with the symbiont telomere probe (TTTAGGG)<sup>*</sup>.</p> <p><strong>4- Mean:</strong> Symbiont mean telomere length measured in kilobases.<sup>*</sup></p> <p><strong>5- standard-deviation: </strong>Standard deviation of the mean measured between the number of measurements done with the symbiont telomere probe (TTAGGG)<sup>*</sup>.</p> <p><strong>6-percentile-50:</strong> Symbiont median telomere length measured in kilobases.<sup>*</sup></p> <p><strong>7-percentile-75:</strong> Symbiont 3<sup>rd</sup> quartile telomere length limit below which lies 75% of the signal, measured in kilobases.<sup>*</sup></p> <p><strong>8-percentile-25:</strong> Symbiont 1<sup>st</sup> quartile telomere length limit below which lies 25% of the signal, measured in kilobases.<sup>*</sup></p> <p><strong>9-interpercentile-p75-p25-distance:</strong> Distance between symbiont_percentile-25 and symbiont_percentile-75 telomere length, measured in kilobases.<sup>*</sup></p> <p><strong><em><sup>*</sup></em></strong><em> When signal was undetected measurements is encoded by “not available”.</em></p>
Tara Pacific 18S-based coral host genetic analysis data release version 1
<p>This dataset contains 4 tables and 3 sets of figures related to the primary analysis of the 18S metabarcoding sequencing output. This dataset is only concerned with the identity of the coral host (i.e. not additional protist diversity). The samples included in this dataset have a 'sample-material_label' value of 'CORAL' and 'sampling-protocol_label' value of 'SEQ-CS4L'. They represent the coral samples collected at all 32 of the islands visited in the Tara Pacific expedition.</p>
Tara Pacific samples provenance and environmental context - version 2
<p>This publication includes the provenance metadata and environmental context of all samples generated by the Tara Pacific Expedition. The metadata fields and parameters are detailed in the readme files. Provenance is given in a single UTF-8 encoded tab-separated-values file. Environmental context is provided in eleven UTF-8 encoded tab-separated-values files, all with the same structure, but each providing a different statistic: </p> <ul> <li>"n" = number of values</li> <li>"mean" = mean value</li> <li>"stdev" = standard deviation</li> <li>"P05" = 5 percentile, i.e. minimum (Q0)</li> <li>"P25" = 25 percentile, i.e. first quartile (Q1)</li> <li>"P50" = 50 percentile, i.e. median (Q2)</li> <li>"P75" = 75 percentile, i.e. third quartile (Q3)</li> <li>"P95" = 95 percentile, i.e. maximum (Q4)</li> <li>"dt" = lag in time, i.e. difference between the collection date/time of the sample and that of the environmental context provided</li> <li>"dxy" = lag in horizontal space, i.e. distance between the collection location of the sample and that of the environmental context provided</li> <li>"dz" = lag in vertical space, i.e. difference between the collection depth/altitude of the sample and that of the environmental context provided</li> </ul> <p>Missing value terms are:</p> <ul> <li>"nav" = not-available, i.e. the expected information is not given because it has not been collected or generated</li> <li>"npr" = not-provided, i.e. the expected information has been collected or generated but it is not given, i.e. a value may be available in a later version or may be obtained by contacting the data providers</li> <li>"nac" = confidential, i.e. the expected information has been collected or generated but is not available openly because of privacy concerns</li> <li>"nap" = not-applicable, i.e. no information is expected for this combination of parameter, environment and/or method, e.g. depth below seabed cannot be informed for a sample collected in the water or the atmosphere</li> </ul>
Tara Pacific Qualitative Photo Annotations
<p>This data is the result of photographic annotations done manually through Matlab for the photographs captured during the Tara Pacific Expedition (2016-2018). More details can be found in the readme file.</p>
Environmental data at the sampling event level collected with Inline instruments, almanach, models and satellites during the Tara Pacific Expedition 2016-2018
<p>The Tara Pacific expedition (2016-2018) sampled coral ecosystems around 32 islands in the Pacific Ocean, and sampled the surface of oceanic waters at 249 locations, resulting in the collection of nearly 58,000 samples. The expedition was designed to systematically study corals, fish, plankton, and seawater, and included the collection of samples for advanced biogeochemical, molecular, and imaging analysis. Here we provide at the sampling event level, the environmental data originating from all instruments acquiring continuously during the full course of the campaign. This dataset is augmented with the addition of variables originating from almanach (local sun/moon set/rise, local zenith), from operational models obtained from Copernicus Marine Services, but also <strong>f</strong>rom satellite imagery (MODIS-AQUA satellite - Level 3 mapped product, 8 day average, 4km resolution) at <a href="https://oceandata.sci.gsfc.nasa.gov">https://oceandata.sci.gsfc.nasa.gov</a>. The zone corresponding to the station position and date was recovered either by taking a two pixel buffer around the given location (total zone being a 5 by 5 pixels square of 20 km side) and in order to propose an alternative measure in the inevitable case where clouds were present an alternative 12 pixels buffer was taken (total zone being a 25 by 25 pixels square of 100 km side). All data were provided as mean, standard deviation (sd) together with 0.05, 0.25, 0.5, 0.75 and 0.95 quartiles</p>
Tara Pacific 16S rRNA ASV table for bacterial communities of crustose coralline algae from the Tuamotu archipelago (French Polynesia)
<p>This data is the result of the primary analysis of the 16S rRNA gene sequencing data collected from the CCA samples collected during the Tara Pacific expedition. The analysis was conducted using cutadapt/snakemake/dada2 and usearch. A full README is contained within the parent data upload (<a href="https://doi.org/10.5281/zenodo.4451892">https://doi.org/10.5281/zenodo.4451892</a>).</p>
TARA Pacific CDIV cnidarian host taxonomic annotation release version 1_1
<p>This data is the result of the primary analysis of the 18SV9 sequencing data and photos associated with the Coral Diversity dataset collected from all islands as part of the Tara Pacific expedition. A full README is contained within the data upload.</p>
TARA Pacific Bleaching Prevalence of Sampling Sites (Islands)
<p><strong>Summary</strong></p> <p>To obtain a proxy for the stress level of collected corals, we checked for previous occurrences of bleaching events at sampled reef sites by matching island GPS coordinates to the Reef Check dataset (reefcheck.org) obtained from Sully et al (2019). For each Tara Pacific island coordinate, we determined the Reef Check site that was closest (in terms of distance in km); we only considered Reef Check data that was within a 10 km circumference. We further determined short- and long-term climate variables that are known to affect coral stress resilience for all <em>Tara</em> Pacific collection sites that are available from Lombard et al (2022). These data allow to assess if corals from a given site were exposed higher/lower prevalence of thermal stress events and bleaching prior to sampling (over previous years).</p> <p><strong>References</strong></p> <p>Sully, S., Burkepile, D. E., Donovan, M. K., Hodgson, G. & van Woesik, R. A global analysis of coral bleaching over the past two decades. <em>Nature Communications</em> <strong>10</strong>, 1264 (2019).</p> <p>Fabien Lombard, Guillaume Bourdin, Stephane Pesant, Sylvain Agostini, Alberto Baudena, Emilie Boissin, Nicolas Cassar, Megan Clampitt, Pascal Conan, Ophélie Da Silva, Celine Dimier, Eric Douville, Amanda Elineau, Jonathan Fin, J. Michel Flores, Jean François Ghiglione, Benjamin C.C. Hume, Laetitia Jalabert, Seth G. John, Rachel L. Kelly, Ilan Koren, Yajuan Lin, Dominique Marie, Ryan McMinds, Zoé Mériguet, Nicolas Metzl, David A. Paz-García, Maria Luiza Pedrotti, Julie Poulain, Mireille Pujo-Pay, Josephine Ras, Gilles Reverdin, Sarah Romac, Eric Röttinger, Assaf Vardi, Christian R. Voolstra, Clémentine Moulin, Guillaume Iwankow, Bernard Banaigs, Chris Bowler, Colomban de Vargas, Didier Forcioli, Paola Furla, Pierre E. Galand, Eric Gilson, Stéphanie Reynaud, Shinichi Sunagawa, Olivier Thomas, Romain Troublé, Rebecca Vega Thurber, Patrick Wincker, Didier Zoccola, Denis Allemand, Serge Planes, Emmanuel Boss, Gaby Gorsky. Open science resources from the Tara Pacific expedition across the surface ocean and coral reef ecosystems. <em>Submitted</em> (2022)</p>
27 MAGs from the Family of Endozoicomonadaceae derived from Tara Pacific Metagenomes
<p>This dataset contains 27 MAGs from the family of Endozoicomonadaceae generated from a subset of Tara Pacific metagenomes.</p> <p>Contextual information of the MAGs can be found in the associated publication: <strong>Ecology of Endozoicomonadaceae in three coral species across the Pacific Ocean</strong>, Hochart et al, submitted</p> <p> </p>
Tara Pacific ITS2 Symbiodiniaceae data release version 1
<p>This data is the result of the primary analysis of the Symbiodiniaceae ITS2 rDNA gene sequencing data collected from all islands as part of the Tara Pacific expedition.The analysis was conducted using SymPortal. A full README is contained within the data upload.</p>
Tara Pacific Biomarker-Based Coral Phenotype Data Release - Pocillopora
<p>Those data correspond to the measurements of the phenotype signatures of <em>Pocillopora </em>colonies collected through 30 Pacific Islands. More precisely, six different biomarkers have been measured for each colony: Animal biomass, Symbiont biomass (normalized by milligram of protein and by centimeter squared), protein carbonylation, total antioxydant capacity, total carbohydrate content and mitochondrial DNA copy number.</p> <p>(XLS version with methodological references)</p>
Tara Pacific CDIV ITS2 Symbiodiniaceae data release
<p>This data is the result of the primary analysis of the ITS2 sequencing data associated with the Coral Diversity dataset collected from all islands as part of the Tara Pacific expedition. A full README is contained within the data upload.</p>
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