This study analyzes the global cargo ship network (GCSN) using data from 16,363 ships in 2007 to understand its structure and implications for global trade and bioinvasion. The network is constructed based on ship itineraries and reveals distinct patterns among three main ship types: container ships, bulk dry carriers, and oil tankers. Container ships follow regular, predictable routes, while bulk dry carriers and oil tankers have less predictable movements. The network exhibits a small-world topology with heavy-tailed distributions for port connectivity and link loads, and it has a multi-layered structure with distinct subnetworks for each ship type. The GCSN is highly clustered, with a high clustering coefficient and short path lengths, and it is more densely connected than other transportation networks like air or road networks. The network shows scale-free properties, with a few highly connected ports handling large volumes of cargo, which makes it vulnerable to the spread of invasive species. The study also compares the GCSN with the gravity model for predicting ship traffic, finding that the real network provides more accurate predictions for bioinvasion spread. The results highlight the importance of understanding the GCSN for managing global trade and reducing the risks of invasive species. The study also shows that different ship types have distinct movement patterns, with container ships having more regular routes and bulk dry carriers and oil tankers having more variable routes. The network's structure and movement patterns have implications for the spread of invasive species, as the high connectivity and clustering of the network can facilitate the spread of invasive organisms. The study also shows that the GCSN is more densely connected than other transportation networks, which contributes to its structural robustness. The findings suggest that the GCSN is a complex network with unique characteristics that are important for understanding global trade and bioinvasion. The study also highlights the importance of using real data to understand the GCSN, as opposed to relying on gravity models which may not capture the full complexity of the network. The results have implications for policy decisions regarding global trade and bioinvasion prevention.This study analyzes the global cargo ship network (GCSN) using data from 16,363 ships in 2007 to understand its structure and implications for global trade and bioinvasion. The network is constructed based on ship itineraries and reveals distinct patterns among three main ship types: container ships, bulk dry carriers, and oil tankers. Container ships follow regular, predictable routes, while bulk dry carriers and oil tankers have less predictable movements. The network exhibits a small-world topology with heavy-tailed distributions for port connectivity and link loads, and it has a multi-layered structure with distinct subnetworks for each ship type. The GCSN is highly clustered, with a high clustering coefficient and short path lengths, and it is more densely connected than other transportation networks like air or road networks. The network shows scale-free properties, with a few highly connected ports handling large volumes of cargo, which makes it vulnerable to the spread of invasive species. The study also compares the GCSN with the gravity model for predicting ship traffic, finding that the real network provides more accurate predictions for bioinvasion spread. The results highlight the importance of understanding the GCSN for managing global trade and reducing the risks of invasive species. The study also shows that different ship types have distinct movement patterns, with container ships having more regular routes and bulk dry carriers and oil tankers having more variable routes. The network's structure and movement patterns have implications for the spread of invasive species, as the high connectivity and clustering of the network can facilitate the spread of invasive organisms. The study also shows that the GCSN is more densely connected than other transportation networks, which contributes to its structural robustness. The findings suggest that the GCSN is a complex network with unique characteristics that are important for understanding global trade and bioinvasion. The study also highlights the importance of using real data to understand the GCSN, as opposed to relying on gravity models which may not capture the full complexity of the network. The results have implications for policy decisions regarding global trade and bioinvasion prevention.