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Cold-Water Coral Community Dynamics


Mounds and margins on the North Atlantic host highly diverse and structurally complex megabenthic communities, such as cold-water coral (CWC) reefs. These communities have been widely impacted by fishing activities, and thus CWC reefs are identified as vulnerable marine ecosystems in need of protection. Recently, several deep-sea environments (e.g. mounts, canyons) hosting CWC reefs, in EU Atlantic waters have been integrated in Marine Protected Areas (MPA). However, few of these MPAs have incorporated management plans. To establish suitable management policies, it is required to implement monitoring strategies that account for these diverse community dynamics and how they may be influenced by environmental drivers. Particulate organic carbon (POC) fluxes are known to shape deep-sea benthic communities, indicating that variations in food supply is a major driver affecting community structure and dynamics. Yet, the relationship between POC fluxes and benthic community dynamics at different temporal and spatial scales are not fully understood. Elucidating deep-sea benthic community dynamics, represents an important challenge for future research especially as management of human impacts becomes a priority. This project’s main goals are to assess the dynamics of CWC communities in mound and margins on the North Atlantic, and examine how different environmental factors may be influencing them. Community dynamics will be assessed via convolutional neural networks which will allow to assess changes in community composition and structural complexity on 3 areas that have been monitored with benthic platforms equipped with HD cameras and several environmental sensors during several months (12 – 24 months). This project will be of wide scientific interest as it will provide insight into the resilience of CWC communities to environmental change and the basis to establish a set of essential variables to be monitored over the long term and support sound conservation actions.

Field of science

  • /natural sciences/computer and information sciences/artificial intelligence/computational intelligence

Call for proposal

See other projects for this call

Funding Scheme

MSCA-IF-EF-ST - Standard EF


Winthontlaan 2
3526 KV Utrecht
Activity type
Research Organisations
EU contribution
€ 187 572,48