Monitoring and visualisation tools are currently attracting more and more attention in order to understand how search spaces are explored by complex optimisation ecosystems such as parallel evolutionary algorithms based on island models. Multilevel visualisation is actually a desirable feature for facilitating the monitoring of computationally expensive runs involving several hundreds of computers during hours or even days. In this paper we present two components of a future multilevel monitoring system: MusEAc, a high level, audio monitoring allowing to listen to a run and tune it in real time and GridVis, a lower lever, more precise a posteriori visualisation tool that lets the user understand why the algorithm has performed well or bad.