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Active STUDENTSHIP UKRI Gateway to Research

Patchiness of marine plankton and its influence on the ocean carbon cycle


Funder Natural Environment Research Council
Recipient Organization University of East Anglia
Country United Kingdom
Start Date Sep 30, 2024
End Date Mar 30, 2028
Duration 1,277 days
Number of Grantees 2
Roles Student; Supervisor
Data Source UKRI Gateway to Research
Grant ID 2929381
Grant Description

Marine ecosystems play a key role in regulating the Earth's climate. Marine ecosystems live largely at the ocean's surface. Their activities generate a flux of carbon between the ocean surface and the deep ocean, so-called export flux, that is as large as the fossil CO2 emissions, and maintains atmospheric CO2 concentrations in the long-term about 200 ppm lower than it would be otherwise.

The most recent global observations reveal that the bulk distribution of plankton populations tends to become more patchy with increasing size of micro-organisms. This suggests a change from small organisms such as picophytoplankton forming a constant background biomass, to large organisms such as jellyfish going through bloom and bust cycles. The patchiness of the ecosystem is thought to be related to extreme events when carbon is exported from the surface to the deep ocean, although this has never been demonstrated.

So far, even the most complex models fail to reproduce the observed increasing patchiness of plankton organisms as a function of their size.

This PhD project aims to better characterise the linkages between patchiness and the size of plankton organisms, identify their drivers, and determine how patchy distributions may be related to carbon export events and influence the ocean carbon cycle. The PhD candidate will examine observations of patchiness using new marine observations such as satellite data , abundance, imaging and genomics.

The candidate will explore the environmental and ecosystem processes driving the regional and temporal variations in the observed patchiness, in particular processes related to viral infections, using both machine-learning techniques and results from a process-based ecosystem model (the PlankTOM12 model). Finally, the candidate will help improve the representation of patchiness in the PlankTOM12 global ecosystem model that currently represents planktonic organisms across all sizes from viruses to bacteria, to six groups of phytoplankton and five zooplankton.

All Grantees

University of East Anglia

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