基于自动识别系统和海洋生态区数据的生物入侵风险分析

IF 3.2 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS
Hongwei Shi , Chenyu Wang , Hang Zhao , Shengling Wang , Yixian Chen
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引用次数: 0

摘要

全球海上贸易在传播外来水生入侵物种方面发挥着关键作用,对环境、人类健康和经济造成了副作用。现有的生物安全方法没有同时考虑入侵风险和入侵物种的扩散,这可能导致生物入侵控制不力。此外,由于没有考虑生物入侵控制对航运的影响,其方法也不符合成本效益。为了解决现有方法存在的问题,我们利用自动识别系统(AIS)数据、压载水数据和水温& 盐度数据构建了两个网络:物种入侵网络(SIN)和全球航运网络(GSN)。前者用于分析港口传播海洋入侵物种的潜力,后者用于评估港口航运的重要性。根据对 SIN 和 GSN 的分析,提出了两类生物安全触发机制。第一类既考虑到生物入侵,也考虑到入侵物种的传播;第二类除了考虑入侵风险,还考虑到每个港口的航运价值。通过大量的案例研究,我们发现了在所提出的生物安全触发机制指导下需要优先控制的关键港口。最后,我们的相关性分析表明,近距离与入侵风险的相关性最高。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Bioinvasion risk analysis based on automatic identification system and marine ecoregion data
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.
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CiteScore
4.70
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