Abstract
The introduction and establishment of non-indigenous species (NIS) through global ship movements is a significant threat to marine ecosystems and economies. While ballast-vectored invasions have been partly addressed by some national policies and an international agreement regulating the concentrations of organisms in ballast water, biofouling-vectored invasions remain a large risk. Development of additional realistic and cost-effective ship-borne NIS policies requires an accurate estimation of NIS spread risk from both ballast water and biofouling. In this paper, we demonstrate that first-order Markov assumptions limit accurate modeling of NIS spread risks through the global shipping network. In contrast, we show that higher-order patterns overcome this limitation by revealing indirect pathways of NIS transfer. We accomplish this by developing Species Flow Higher-Order Networks (SF-HON), which we developed independently for ballast and biofouling, for comparison with first-order Markovian models of ballast and biofouling. We evaluated SF-HON predictions using the largest available datasets of invasive species for Europe and the United States. We show that not only does SF-HON yield more accurate NIS spread risk predictions than first-order models and existing higher-order models, but also that there are important differences in NIS spread via the ballast and biofouling vectors. Our work provides information that policymakers can use to develop more efficient and targeted prevention strategies for ship-borne NIS spread management, especially as management of biofouling is of increasing concern.
Mandana Saebi
Machine Learning Engineer
My research interests include machine learning, knowledge graphs.