12 The global cargo ship network can become particularly pronounced for ports which are well-connected, but not among the central hubs in the network (Fig. 7b). Comparing typical sequences of infected ports, we find that the invasions driven by the real traffic flows tend to be initially confined to smaller regional ports, whereas in the gravity model the invasions quickly reach the hubs. The total out- and in-flows at the ship journeys’ origin and departure ports, respectively, are indeed more strongly positively correlated in reality than in the model ( τ = 0 . 157 vs. 0 . 047). The gravity model thus erases too many details of a hierarchical structure present in the real network. That the gravity model eliminates most correlations, is also plausible from simple analytic arguments, see Electronic Supplementary Material for details. The absence of strong correlations makes the gravity model a suitable null hypothesis if the correlations in the real network are unknown, but several recent studies have shown that correlations play an important role in spreading processes on networks (e.g. Newman 2002, Bogu˜ na & Pastor-Satorras 2002). Hence, if the correlations are known, they should not be ignored. While we observed that the spreading rates for the AIS data were consistently slower than for the gravity model even when different parameters or population models were considered, the time scale of the invasion is much less predictable. The assumption that only a small fraction of invaders succeed outside their native habitat appears realistic (Mack et al. 2000). Furthermore, the parameters in our model were adjusted so that the per-ship-call probabil- ity of initiating invasion is approximately 4 . 4 · 10 − $^{4}$, a rule-of-thumb value stated by Drake & Lodge (2004). Still, too little is empirically known to pin down individual parameters with sufficient accuracy to give more than a qualitative impression. It is especially difficult to predict how a potential invader reacts to the environmental conditions at a specific lo- cation. Growth rates certainly differ greatly between ports depending on factors such as temperature or salinity, with respect to the habitat requirements of the invading organisms. Our results should, therefore, be regarded as one of many different conceivable scenarios. A more detailed study of bioinvasion risks based on the GCSN is currently underway (Seebens & Blasius 2009). We have presented a study of ship movements based on AIS records. Viewing the ports as nodes in a network linked by ship journeys, we found that global cargo shipping, like many VII. CONCLUSION