Visualising continuous intra-landscape isolation with uncertainty using least-cost modelling based catchment areas: common brushtail possums in the Auckland isthmus

摘要

Invasive species have become a major stressor in many ecosystems. Therefore, public-policy decision-makers desire ecologically informed risk assessments that characterise the likelihood and severity of potential adverse effects from invasive species. These risk assessments often take the form of maps, but risk maps have generally ignored uncertainty and avoided incorporating processes such as the risk of spread. One method that has been proposed to map risk of spread is a least-cost modelling based catchment area approach. In summary, using a raster cost-surface that represents the difficulty associated with traversing different parts of a landscape, least-cost catchments are calculated for all cells in a landscape, and the catchments' areas are then visualised as a map of intra-landscape isolation. However, there are challenges with parameterising least-cost modelling in an ecological context which means it is particularly important that any estimates of isolation are coupled with associated estimates of uncertainty. Using an example of the common brushtail possum (Trichosurus vulpecula) on the Auckland isthmus (New Zealand), we provide a demonstration of how the least-cost modelling based catchment area approach to mapping isolation can be applied to estimate isolation with uncertainty. We use a Python geoprocessing and geovisualisation framework to quantify isolation for four least-cost modelling based possum dispersal scenarios in order to produce a bivariate colour-by-alpha map that simultaneously visualises predicted isolation with associated uncertainty. The bivariate colour-by-alpha map clearly reinforces the need to consider uncertainty when producing catchment area isolation maps. While the overall pattern in isolation was a relatively simple function of the different isolation maps, the associated uncertainty had a complex spatial structure that would be difficult to understand without representing it explicitly. Providing policy decision-makers with bivariate maps that simultaneously include isolation and uncertainty will help support more robust invasive species risk assessments.