Patterns of Ship-borne Species Spread: A Clustering Approach for Risk Assessment and Management of Non-indigenous Species Spread.

The spread of non-indigenous species (NIS) through the global shipping network (GSN) has enormous ecological and economic cost throughout the world. Previous attempts at quantifying NIS invasions have mostly taken "bottom-up" approaches that eventually require the use of multiple simplifying assumptions due to insufficiency and/or uncertainty of available data. By modeling implicit species exchanges via a graph abstraction that we refer to as the Species Flow Network (SFN), a different approach that exploits the power of network science methods in extracting knowledge from largely incomplete data is presented. Here, coarse-grained species flow dynamics are studied via a graph clustering approach that decomposes the SFN to clusters of ports and inter-cluster connections. With this decomposition of ports in place, NIS flow among clusters can be very efficiently reduced by enforcing NIS management on a few chosen inter-cluster connections. Furthermore, efficient NIS management strategy for species exchanges within a cluster (often difficult due higher rate of travel and pathways) are then derived in conjunction with ecological and environmental aspects that govern the species establishment. The benefits of the presented approach include robustness to data uncertainties, implicit incorporation of "stepping-stone" spread of invasive species, and decoupling of species spread and establishment risk estimation. Our analysis of a multi-year (1997--2006) GSN dataset using the presented approach shows the existence of a few large clusters of ports with higher intra-cluster species flow that are fairly stable over time. Furthermore, detailed investigations were carried out on vessel types, ports, and inter-cluster connections. Finally, our observations are discussed in the context of known NIS invasions and future research directions are also presented.