Aquatic Animal Pest and Disease Readiness Planning and Intelligence Phase II. Morrisey, D., Plew, D., & Seaward, K. Technical Report No: 2011/68, Wellington, New Zealand., July, 2011.
Aquatic Animal Pest and Disease Readiness Planning and Intelligence Phase II [link]Paper  abstract   bibtex   
Phase I consited of data collection in a suitable format to underpin effective surveillance, incursion investigation and response, and biosecurity readiness work for cultured and enhanced aquatic species. The data collected in Phase I are used in Phase II to create ‘defined areas’ in which aquacultured organisms have a similar likelihood of exposure to a pest or disease. A three-stage approach was used to derive the defined dispersion areas. In the first stage, we reviewed relevant examples of disease and pest preparedness work/research. This guided our development of dispersion areas in the second stage, in which GIS layers representing the geographical distribution of aquaculture facilities and vectors of disease and pest movement were established. Potential vectors include hydrodynamic features of aquacultural areas, anthropogenic vectors, and other relevant environmental factors, such as habitat types that are likely to influence transmission of pests and diseases. The third stage integrated hydrodynamic layers to derive the proposed dispersion areas. Input from MAFBNZ and other stakeholders to the process was sought at workshops held in Nelson and Auckland in late 2010. The consensus was that the simplest modelling option for marine farms should be adopted and applied nationwide – i.e. dispersion would be modelled on the basis of tidal advection. Modelling of dispersion by downstream drift in rivers used flow rates (mean, mean annual maximum, and mean annual low flow) to derive downstream dispersion distances, assuming 1–3 days infectious life. This included land-based facilities that discharge into waterways.
@techreport{morrisey_aquatic_2011,
	address = {Wellington, New Zealand.},
	type = {{MAF} {Biosecurity} {New} {Zealand} {Technical} {Paper}},
	title = {Aquatic {Animal} {Pest} and {Disease} {Readiness} {Planning} and {Intelligence} {Phase} {II}},
	url = {https://www.mpi.govt.nz/dmsdocument/15751-aquaculture-readiness-data-project-report-phase-2},
	abstract = {Phase I consited of data collection in a suitable format to underpin effective surveillance,
incursion investigation and response, and biosecurity readiness work for cultured and
enhanced aquatic species. The data collected in Phase I are used in Phase II to create ‘defined
areas’ in which aquacultured organisms have a similar likelihood of exposure to a pest or
disease. A three-stage approach was used to derive the defined dispersion areas. In the first
stage, we reviewed relevant examples of disease and pest preparedness work/research. This
guided our development of dispersion areas in the second stage, in which GIS layers
representing the geographical distribution of aquaculture facilities and vectors of disease and
pest movement were established. Potential vectors include hydrodynamic features of
aquacultural areas, anthropogenic vectors, and other relevant environmental factors, such as
habitat types that are likely to influence transmission of pests and diseases. The third stage
integrated hydrodynamic layers to derive the proposed dispersion areas. Input from MAFBNZ
and other stakeholders to the process was sought at workshops held in Nelson and Auckland
in late 2010. The consensus was that the simplest modelling option for marine farms should
be adopted and applied nationwide – i.e. dispersion would be modelled on the basis of tidal
advection. Modelling of dispersion by downstream drift in rivers used flow rates (mean, mean
annual maximum, and mean annual low flow) to derive downstream dispersion distances,
assuming 1–3 days infectious life. This included land-based facilities that discharge into
waterways.},
	number = {No: 2011/68},
	urldate = {2020-12-15},
	author = {Morrisey, Donald and Plew, David and Seaward, Kimberley},
	month = jul,
	year = {2011},
	pages = {64},
}

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