Plank2Space

Every liter of seawater contains between 10 and 100 billion interacting life forms, including tens of thousands of species of viruses, bacteria, archaea, protists and animals – most of them invisible to the human eye. This ocean invisible life drives global biogeochemical cycles and planetary ecosystems functions such as the biological carbon pump. Over the past two decades, DNA/RNA sequencing-based methods have been extensively applied in ocean science, demonstrating their power to assess the diversity and complexity of plankton life across the full range of organismal size and taxonomy. Initial machine-learning attempts to link emerging global-scale, DNA-based in-situ plankton biodiversity data with remote-sensing ocean color are promising, but strongly limited by the heterogeneity of existing plankton DNA-based datasets, and the very limited number of in-situ biodiversity measurements that matches satellite data. PlanktoSpace proposes a global, distributed, cost-effective, agile, and scalable approach to generate the largest consistent in-situ dataset of direct meta-omics biodiversity matchups to ocean color, providing the amounts of data required to develop algorithms capable of predicting ecosystem biotic composition from space. In collaboration with a NASA-funded starting effort, we aim to equip 20 ships of opportunity, primarily sailing boats, with novel, cost-effective and user-friendly instruments to sample plankton communities for DNA-sequencing analyses back in the lab and for semi-quantitative imaging analyses at sea.

The trained seatizen crews will cover as many states of the plankton ecosystem and biogeochemical provinces as possible from the surface ocean, guided by a PlanktoSpace App that will inform them when time and atmospheric conditions are optimal in terms of matchup with satellites. Over a two-year period, the PlanktoSpace fleet (including ships funded by NASA sister project) is expected to gather standardized plankton biodiversity datasets from more than 1200 ‘matchup sites’ covering the world’s ocean. From each site, standardized DNA-metabarcoding datasets will be generated using a protocol capable of quantifying marker genes assessing the entire tree of life – encompassing prokaryotes, eucaryotes, and their organelles. We will then employ supervised machine-learning techniques to correlate remote sensing reflectances (hyperspectral from PACE, and multispectral from S2, S3, and OC-CCI) with matched in-situ tree-of-life biodiversity data. Sensor-specific global ocean experimental datasets will be generated, validated, and published in a high-impact study showcasing the power of biodiversity proxies derived from hyper/multi-spectral and tree-of-life data-based algorithms to capture the ecological complexity and dynamics of ocean life.

Last but not least, PlanktoSpace will introduce international seafarers to the world ocean’s invisible life and satellite-based observations, fostering citizen engagement into a vibrant ESA-NASA collaboration aiming at developing the next-generation tools for monitoring, forecasting, and protecting ocean biodiversity and health from space.