Quantitative imaging datasets of surface micro- to mesoplankton communities and microplastic across the Pacific and North Atlantic oceans from the Tara Pacific expedition
<p>This paper presents the quantitative imaging datasets collected during the Tara Pacific expedition (2016–2018) carried out on the schooner <i>Tara</i>. The datasets cover a wide range of plankton sizes, from microphytoplankton (<span class="inline-formula">&g...
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| Main Authors: | , , , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Copernicus Publications
2025-06-01
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| Series: | Earth System Science Data |
| Online Access: | https://essd.copernicus.org/articles/17/2761/2025/essd-17-2761-2025.pdf |
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| Summary: | <p>This paper presents the quantitative imaging datasets collected during the Tara Pacific expedition (2016–2018) carried out on the schooner <i>Tara</i>. The datasets cover a wide range of plankton sizes, from microphytoplankton (<span class="inline-formula">></span> 20 <span class="inline-formula">µ</span>m in size) to mesozooplankton (a few centimetres in size), and non-living particles such as plastic and detrital particles. It consists of surface samples collected across the North Atlantic and the North and South Pacific Ocean from open-ocean stations (a total of 357 samples) and from stations located in coastal waters, lagoons or reefs of 32 Pacific islands (a total of 228 samples). As this expedition involved long distances and long sailing times, we designed two sampling systems to collect plankton while sailing at speeds of up to 9 knots. To sample microplankton, surface water was pumped aboard using a customised pumping system and filtered through a 20 <span class="inline-formula">µ</span>m mesh size plankton net (hereafter referred to as the deck net – DN). A high-speed net (HSN; 330 <span class="inline-formula">µ</span>m mesh size) was developed to sample the mesoplankton. In addition, a manta net (330 <span class="inline-formula">µ</span>m) was also used, when possible, to collect mesoplankton and plastics simultaneously. We could not deploy these nets at the reef and lagoon stations of islands. Instead, two bongo nets (20 <span class="inline-formula">µ</span>m) attached to an underwater scooter were used to sample microplankton. In addition to describing and presenting the datasets, the complementary aim of this paper is to investigate and quantify the potential sampling biases associated with these two high-speed sampling systems and the different net types, in order to improve further ecological interpretations. Regarding the imaging techniques, microplankton (20–200 <span class="inline-formula">µ</span>m) from the DN and bongo net were imaged directly aboard <i>Tara</i> using a FlowCam instrument (Fluid Imaging Technologies), whereas mesoplankton (<span class="inline-formula">>200</span> <span class="inline-formula">µ</span>m) from the HSN and manta net were analysed in the laboratory with a ZooScan system (back on land). Organisms and other particles were taxonomically and morphologically classified using the automatic sorting tools of the EcoTaxa web application; following this, validation or correction was carried out by taxonomic experts. For microplankton smaller than 45 <span class="inline-formula">µ</span>m, a subsample of 30 % of the annotations was 100 % visually validated by experts. More than 300 different taxonomic and morphological groups were identified. The datasets include the metadata and<span id="page2762"/> the raw data from which morphological traits such as size (equivalent spherical diameter) and biovolume were calculated for each particle as well as a number of quantitative descriptors of the surface plankton communities. These descriptors include abundance, biovolumes, the Shannon diversity index and normalised biovolume size spectrum, allowing the study of their structures (e.g. taxonomic, functional, size and trophic structures) according to a wide range of environmental parameters at the basin scale (<a href="https://doi.org/10.5281/zenodo.6445609">https://doi.org/10.5281/zenodo.6445609</a>, Lombard et al., 2023).</p> |
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| ISSN: | 1866-3508 1866-3516 |