Bayesian network modelling provides spatial and temporal understanding of ecosystem dynamics within shallow shelf seas

Bayesian network modelling provides spatial and temporal understanding of ecosystem dynamics within shallow shelf seas

Understanding ecosystem dynamics within shallow shelf seas is of great importance to support marine spatial management of natural populations and activities such as fishing and offshore renewable energy production to combat climate change. Given the possibility of future changes, a baseline is neede...

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Bibliographic Details
Main Authors: De Dominicis, M. (Author), Scott, B.E (Author), Trifonova, N.I (Author), Waggitt, J.J (Author), Wolf, J. (Author)
Format: Article
Language:English
Published: Elsevier B.V. 2021
Subjects:
alternative energy
Atlantic Ocean
Bayesian analysis
Bayesian networks
Biological variables
chlorophyll a
Climate change
Data driven
disturbance
Dynamics
Ecosystem changes
ecosystem dynamics
Ecosystem dynamics
Ecosystems
Fisheries
Fisheries effects
Fishery effect
Functional ecosystem change
Hidden variable
Indicator indicator
Molecular physics
North Sea
Oceanography
Offshore oil well production
Phocoenidae
Physical variables
potential energy
Potential energy
primary production
sea surface temperature
shelf sea
Shelf seas
spatiotemporal analysis
Surface waters
Top predator dynamic
Top predator dynamics
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