Data from: Sensitivity of marine protected area network connectivity to atmospheric variability

• Main Authors: Fox, Alan D., Henry, Lea-Anne, Corne, David W., Roberts, J. Muray, Roberts, J. Murray
• Format: Other/Unknown Material
• Language: unknown
• Published: Zenodo 2016
• Subjects: inter-annual variability; cold-water coral; Marine protected area; North Atlantic Oscillation; particle tracking; Lophelia pertusa; North Atlantic; North Atlantic oscillation; Northeast Atlantic
• Online Access: https://doi.org/10.5061/dryad.2hf38

International efforts are underway to establish well-connected systems of marine protected areas (MPAs) covering at least 10% of the ocean by 2020. But the nature and dynamics of ocean ecosystem connectivity are poorly understood, with unresolved effects of climate variability. We used 40-year runs of a particle tracking model to examine the sensitivity of an MPA network for habitat-forming cold-water corals in the northeast Atlantic to changes in larval dispersal driven by atmospheric cycles and larval behaviour. Trajectories of Lophelia pertusa larvae were strongly correlated to the North Atlantic Oscillation (NAO), the dominant pattern of interannual atmospheric circulation variability over the northeast Atlantic. Variability in trajectories significantly altered network connectivity and source–sink dynamics, with positive phase NAO conditions producing a well-connected but asymmetrical network connected from west to east. Negative phase NAO produced reduced connectivity, but notably some larvae tracked westward-flowing currents towards coral populations on the mid-Atlantic ridge. Graph theoretical metrics demonstrate critical roles played by seamounts and offshore banks in larval supply and maintaining connectivity across the network. Larval longevity and behaviour mediated dispersal and connectivity, with shorter lived and passive larvae associated with reduced connectivity. We conclude that the existing MPA network is vulnerable to atmospheric-driven changes in ocean circulation. RSOS160494_Python_Code Python scripts for the particle tracking model and data processing.