Comparing temporal and aggregated network descriptions of fluid transport in the Mediterranean Sea

TL;DR

This study compares temporal and aggregated network models of fluid transport in the Mediterranean Sea, revealing that aggregation can obscure important temporal features affecting connectivity and tracer dispersal.

Contribution

It demonstrates how aggregation alters network structure and impacts the accuracy of transport and dispersal models compared to explicit temporal networks.

Findings

Aggregation causes structural network changes that are hard to avoid.

Temporal features significantly influence network connectivity depending on time scales.

Temporal dispersal kernels better capture transient tracer dynamics than aggregated matrices.

Abstract

Ocean currents exhibit strong time dependence at all scales that influences physical and biochemical dynamics. Network approaches to fluid transport permit to address explicitly how connectivity across the seascape is affected by the spatiotemporal variability of currents. However, such temporal aspect is mostly neglected, relying on a static representation of the flow. We here investigate the role of current variability on networks describing physical transport across the Mediterranean basin. We first focus on degree distributions and community structure comparing ensembles of temporal networks that explicitly resolve time dependence and their aggregated, i.e. time-averaged, counterparts. Furthermore, we explore the implications of the two approaches in a simple reaction dispersal model for a generic tracer. Our analysis evidences that aggregation induces structural network changes that cannot be easily avoided, not even introducing a pruning of the aggregated adjacency matrix. We also highlight that, depending on the time scales considered, the importance of the temporal features of the networks can vary significantly. Finally, we find that the tracer evolution obtained from a temporal dispersal kernel cannot be always approximated by aggregated adjacency matrices, in particular during transients of the dynamics.