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      ONEPlankton - interconnecting methods for high-throughput phytoplankton diversity and abundance</h1>
              
      
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                          <p>Contact person: <a href="mailto:a.l.d.santos@ibv.uio.no">Adriana Lopes dos Santos</a><br/>
Keywords: Machine learning, automated imaging system, metabarcoding, ocean color<br/>
Research group: Adriana Lopes dos Santos (AQUA/IBV), Alex Eiler (AQUA/IBV), Jan Heuschele (AQUA/IBV), Bente Edvardsen (AQUA/IBV), Daniel Vaulot (CNRS, FR2424,France), Ian Probert (CNRS, FR2424, France), Wenche Eikrem (NIVA)<br/>
Department of Biosciences (IBV)<br/>
&nbsp;</p>

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                          <p>Monitoring global dynamics of marine plankton, both in coastal and oceanic regions, &nbsp;is fundamental to understanding the interplay between their ecosystem services (e.g., carbon sequestration) and future climate scenarios. Phytoplankton communities have been historically monitored at different spatial and temporal scales in view of their structural complexity and dynamics. On the one hand, microscopic individual cell identification and counts &nbsp;by specialists have been used by researchers and in monitoring programs for several years and recently complemented by underwater imaging flow cytometers (IFC). IFC provides high-resolution individual cell counts, image, volume, and pigments content. On the other hand, high-throughput sequencing data have provided a wealth of data on the fine scale taxonomy of phytoplankton communities at local and global scales. However, these types of datasets are still very much disjunct and used in isolation. We believe the development of methods interconnecting these two types of data is fundamental for the development of unified plankton long-term observatories. These are very much needed in view of the rapid changes taking place e.g., in coastal environments such as Oslofjorden.</p>

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                          <h4>Topics from methodological research:&nbsp;</h4>

<ul>
	<li>Machine learning and AI</li>
	<li>High-throughput DNA sequencing</li>
	<li>Databases and data sharing</li>
	<li>Functional trait-based approaches</li>
	<li>Ecosystem models</li>
</ul>

<h4>Topics in life science:</h4>

<ul>
	<li>Dynamic solutions for ocean and coastal monitoring</li>
	<li>High taxonomic resolution of in situ images</li>
	<li>One health framework to assess the impact of climate-driven changes in plankton communities</li>
	<li>Development and Improvement of image and sequence databases for plankton biodiversity surveys (e.g., PR2, EcoTaxa)</li>
</ul>

<h4>External Partners:</h4>

<ul>
	<li>Norwegian Institute of Water Research (NIVA)</li>
	<li>Norwegian University of Science and Technology (NTNU)</li>
	<li>University of Bergen, Department of Biological Sciences (Marine Microbiology group)</li>
	<li>NORCE (Climate and Environment)</li>
	<li>Roscoff Culture Collection (Station Biologique de Roscoff, France)</li>
</ul>

<p>&nbsp;</p>

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