Biosecurity risks posed by a large sea-going passenger vessel: challenges of terrestrial arthropod species detection and eradication.
Simon J McKirdySimon O'ConnorMelissa L ThomasKristin L HortonAngus WilliamsDarryl HardieGrey T CouplandJohann van der MerwePublished in: Scientific reports (2019)
Large sea-going passenger vessels can pose a high biosecurity risk. The risk posed by marine species is well documented, but rarely the risk posed by terrestrial arthropods. We conducted the longest running, most extensive monitoring program of terrestrial arthropods undertaken on board a passenger vessel. Surveillance was conducted over a 19-month period on a large passenger (cruise) vessel that originated in the Baltic Sea (Estonia). The vessel was used as an accommodation facility to house workers at Barrow Island (Australia) for 15 months, during which 73,061 terrestrial arthropods (222 species - four non-indigenous (NIS) to Australia) were collected and identified on board. Detection of Tribolium destructor Uytt., a high-risk NIS to Australia, triggered an eradication effort on the vessel. This effort totalled more than 13,700 human hours and included strict biosecurity protocols to ensure that this and other non-indigenous species (NIS) were not spread from the vessel to Barrow Island or mainland Australia. Our data demonstrate that despite the difficulties of biosecurity on large vessels, stringent protocols can stop NIS spreading from vessels, even where vessel-wide eradication is not possible. We highlight the difficulties associated with detecting and eradicating NIS on large vessels and provide the first detailed list of species that inhabit a vessel of this kind.
Keyphrases
- helicobacter pylori infection
- public health
- genetic diversity
- machine learning
- endothelial cells
- climate change
- loop mediated isothermal amplification
- real time pcr
- electronic health record
- big data
- quality improvement
- artificial intelligence
- deep learning
- sensitive detection
- risk assessment
- high intensity
- data analysis