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Article << Previous     |     Next >>   Contents Vol 40(7)

Spatial prediction of brushtail possum (Trichosurus vulpecula) distribution using a combination of remotely sensed and field-observed environmental data

Thibaud Porphyre A D, Joanna McKenzie A, Andrea E. Byrom B, Graham Nugent B E, James Shepherd C and Ivor Yockney B

A EpiCentre, Massey University, Private Bag 11222, Palmerston North 4442, New Zealand.
B Landcare Research, PO Box 69, Lincoln 8152, New Zealand.
C Landcare Research, Private Bag 11052, Palmerston North 4442, New Zealand.
D Present address: Epidemiology Group, Centre for Immunity, Infection and Evolution, University of Edinburgh, Ashworth Laboratories, Edinburgh, United Kingdom.
E Corresponding author. Email: nugentg@landcareresearch.co.nz

Wildlife Research 40(7) 578-587 http://dx.doi.org/10.1071/WR13028
Submitted: 12 February 2013  Accepted: 25 November 2013   Published: 23 December 2013

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Context: In New Zealand, the introduced brushtail possum, Trichosurus vulpecula, is a reservoir of bovine tuberculosis and as such poses a major threat to the livestock industry. Aerial 1080 poisoning is an important tool for possum control but is expensive, creating an ongoing need for ever more cost-effective ways of using this technique.

Aims: To develop geographic information system (GIS) models to better predict spatial variation in the distribution of unmanaged possum populations, to facilitate better targeting of control activities.

Methods: Relative abundance of possums and their distribution among habitat types were surveyed in a dry high-country area of the northern South Island. Two GIS-based models were developed to predict the relative abundance of possums on trap lines. The first model used remotely sensed (digital) environmental data; the second complemented the remotely sensed data with fine-scale habitat and topographic data collected on the ground.

Key results: Digital environmental factors and habitat features proved to be key predictors of relative possum abundance. In both GIS models, height above valley floor, presence of forest cover and mean annual temperature were the strongest predictors.

Conclusions: Predictive maps (projections) of relative possum abundance produced from these models can provide useful decision-support tools for pest-control managers, by enabling possum control to be targeted spatially.

Implications: Spatially targeted pest control could allow effective control activities for invasive species or disease vectors to be applied at a lower cost for the same benefit.

Additional keywords: disease management, invasive species, predictive map, relative abundance, spatial modeling, spatially targeted control, species distribution, vertebrate pest.


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