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handle: 10261/173514
Abstract Lagrangian Flow Network (LFN) is a modelling framework in which ocean sub-areas are represented as nodes in a network interconnected by links representing transport of propagules (eggs and larvae) by currents. We asses the sensitivity and robustness of four LFN-derived connectivity metrics measuring retention and exchange. The most relevant parameters are tested over large ranges and a wide region with contrasting hydrodynamics: density of released particles, node size (spatial scale of discretization), Pelagic Larval Duration (PLD) and spawning modality. We find a minimum density of released particles that guarantees reliable values for most of the metrics examined. We also find that node size has a nontrivial influence on them. Connectivity estimates for long PLDs are more robust against biological uncertainties (PLD and spawning date) than for short PLDs. For mass-spawners releasing propagules over short periods (≈ 2-10 days), daily release must be simulated to properly consider connectivity fluctuations due to variable currents. In contrast, average connectivity estimates for species that spawn repeatedly over longer durations (few weeks to few months) remain robust even using longer periodicity (5-10 days). Our results have implications to design connectivity experiments with particle-tracking models and to evaluate the reliability of their results.
Lagrangian Flow Network, Connectivity, [SDU.OCEAN] Sciences of the Universe [physics]/Ocean, Atmosphere, larval dispersion and transport, particle-tracking model, robustness, Bio-physical model, Larval dispersion and transport, bio-physical model, sensitivity analysis, connectivity, Particle-tracking model, Robustness, Sensitivity analysis
Lagrangian Flow Network, Connectivity, [SDU.OCEAN] Sciences of the Universe [physics]/Ocean, Atmosphere, larval dispersion and transport, particle-tracking model, robustness, Bio-physical model, Larval dispersion and transport, bio-physical model, sensitivity analysis, connectivity, Particle-tracking model, Robustness, Sensitivity analysis
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