Bioinvasion risk analysis based on automatic identification system and marine ecoregion data

Abstract

The global maritime trade plays a key role in propagating alien aquatic invasive species, which incurs side effects in terms of environment, human health and economy. The existing biosecurity methods did not take into account the invaded risk as well as the diffusion of invasive species at the same time, which may lead to inadequate bioinvasion control. In addition, the lack of considering the impact of bioinvasion control on shipping also makes their methods cost-ineffective. To solve the problems of the existing methods, we employ the automatic identification system (AIS) data, the ballast water data and the water temperature & salinity data to construct two networks: the species invasion network (SIN) and the global shipping network (GSN). The former is used to analyze the potential of a port in propagating marine invasive species while the latter is employed to evaluate the shipping importance of ports. Based on the analysis of SIN and GSN, two categories of biosecurity triggering mechanisms are proposed. The first category takes into consideration both being bioinvaded and spreading invasive species and the second one concerns the shipping value of each port besides its invasion risk. A lot of case studies have been done to discover the key ports needed to be controlled preferentially under the guide of the proposed biosecurity triggering mechanisms. Finally, our correlation analysis shows that closeness is most highly correlated to the invasion risk.

Keywords

Biological invasion / Species invasion network / Global shipping network

Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Acknowledgments

This work has been supported by the National and Natural Science Foundation of China (61772044, 62077044, and 62293555), the Major Program of Science and Technology Innovation 2030 of China (2022ZD0117105) and the Major Program of Natural Science Research Foundation of Anhui Provincial Education Department, China (2022AH040148).

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