College of Information Science and Technology, Beijing Normal University, Beijing, 100875, China.
Department of Computer Science, Texas Christian University, Fort Worth, 298850, TX, USA.
BMC Bioinformatics. 2018 Aug 13;19(Suppl 9):287. doi: 10.1186/s12859-018-2272-5.
Global maritime trade plays an important role in the modern transportation industry. It brings significant economic profit along with bioinvasion risk. Species translocate and establish in a non-native area through ballast water and biofouling. Aiming at aquatic bioinvasion issue, people proposed various suggestions for bioinvasion management. Nonetheless, these suggestions only focus on the chance of a port been affected but ignore the port's ability to further spread the invaded species.
To tackle the issues of the existing work, we propose a biosecurity triggering mechanism, where the bioinvasion risk of a port is estimated according to both the invaded risk of a port and its power of being a stepping-stone. To compute the invaded risk, we utilize the automatic identification system data, the ballast water data and marine environmental data. According to the invaded risk of ports, we construct a species invasion network (SIN). The incoming bioinvasion risk is derived from invaded risk data while the invasion risk spreading capability of each port is evaluated by s-core decomposition of SIN.
We illustrate 100 ports in the world that have the highest bioinvasion risk when the invaded risk and stepping-stone bioinvasion risk are equally treated. There are two bioinvasion risk intensive regions, namely the Western Europe (including the Western European margin and the Mediterranean) and the Asia-Pacific, which are just the region with a high growth rate of non-indigenous species and the area that has been identified as a source for many of non-indigenous species discovered elsewhere (especially the Asian clam, which is assumed to be the most invasive species worldwide).
全球海上贸易在现代运输业中发挥着重要作用。它带来了巨大的经济效益，但也带来了生物入侵的风险。通过压载水和生物附着，物种在非本地区域迁移和定居。针对水生生物入侵问题，人们提出了各种生物入侵管理建议。然而，这些建议仅关注港口受影响的可能性，而忽略了港口进一步传播入侵物种的能力。
为了解决现有工作中的问题，我们提出了一个生物安保触发机制，根据港口的入侵风险及其作为跳板的能力来估计港口的生物入侵风险。为了计算入侵风险，我们利用了自动识别系统数据、压载水数据和海洋环境数据。根据港口的入侵风险，我们构建了一个物种入侵网络（SIN）。传入的生物入侵风险来自于入侵风险数据，而每个港口的入侵风险传播能力则通过 SIN 的 s-核心分解来评估。
当同等考虑入侵风险和跳板生物入侵风险时，我们说明了全球 100 个港口具有最高的生物入侵风险。有两个生物入侵风险密集区域，即西欧（包括西欧边缘和地中海）和亚太地区，这两个区域正是非本地物种增长率较高的地区，也是被确定为许多非本地物种的来源地（尤其是亚洲蛤蜊，被认为是全球最具入侵性的物种）。
