ONEPlankton - interconnecting methods for high-throughput phytoplankton diversity and abundance
Contact person: Adriana Lopes dos Santos
Keywords: Plankton, ocean, automated imaging system, metabarcoding, ocean color
Research group: Aquatic biology and toxicology (AQUA)
Department of Biosciences
Monitoring global dynamics of marine plankton, both in coastal and oceanic regions, 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. Ocean surface color derived from satellite data has been used to monitor phytoplankton primary production and biomass at ocean scales over decades. On the one hand, microscopic individual cell identification and counts by specialists have been used by researchers and in monitoring programs for several years and recently complemented by underwater imaging flow cytometers (IFC) which provides high-resolution 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 three types of data is fundamental for the development of unified plankton long-term observatories and a step forward to a sustainable ocean future.
Research proposals may span several methodological approaches within this scope, including:
- Predictive models of phytoplankton community composition and abundance combining satellite-derived, microscopy and sequencing data.
- Models for automatic phytoplankton identification at fine taxonomic level from in situ imaging data.
- From taxonomic to functional diversity - data mining of cell traits (e.g. cell size, trophic mode) from imaging systems and sequencing data.
External partners:
- Norwegian University of Science and Technology (NTNU, Autonomous underwater monitoring using optical sensors)
- University of Bergen. Department of Biological Sciences (Marine Microbiology group)
- Norwegian Institute of Water Research (NIVA, Long term monitoring microscopic data and in-situ automated submersible imaging flow cytometer from Norwegian coastal waters)
- NORCE (Climate and Environment)
Mentoring and internship will be offered by a relevant external partner.