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Big data analysis for evaluating bioinvasion risk
Wang, Shengling Wang, Chenyu Wang, Shenling Ma, Liran
Wang, Shengling
Wang, Chenyu
Wang, Shenling
Ma, Liran
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BioMed Central
Date
2018-08-13
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Abstract
Background 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. Results 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. Conclusions 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).
Contents
Subject
Bioinvasion
Species invasion network
S-core decomposition
Species invasion network
S-core decomposition
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Description
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Computer Science