The use of habitat analysis in the control of wildlife tuberculosis in New Zealand : a thesis presented in partial fulfilment of the requirements for the degree of Doctor of Philosophy at Massey University
The brushtail possum (Trichosurus vulpecula) acts as a wildlife reservoir of Mycobacterium bovis infection for farmed livestock and other wildlife species in areas covering an estimated 23 percent of New Zealand. This reservoir of infection is severely hampering efforts to eradicate TB from farmed animals in New Zealand. New strategies need to be developed that focus on identifying areas of residual TB infection in possums and eradicating them, while maintaining low broad-scale population levels in the most cost-effective way. This thesis comprise a number of studies that apply multivariate modelling techniques and spatial analytical procedures to evaluate spatial patterns of possum habitat and of TB occurrence in cattle herds, and hence to identify high-risk localities for possum-derived TB and target control strategies of varying intensity where each will be most cost-effective. An initial case-control study of 132 possum capture sites was used to identify habitat and topographic factors at the scale of the possum home range that were predictors of the probability of capturing tuberculous possums. Data on the following factors were collected from a 50 meter square area at each capture site: abundance of plant species and structural features of vegetation, the abundance and quality of available den sites, and topographic factors such as slope and aspect. Data were analysed using unconditional logistic regression modelling. Slope of the land and factors associated with the quality of dens were associated with the probability of capturing multiple TB possums. Slope of the land, height of trees, percent cover of vegetation in the height range 0.3 - 2 metres and percent of the ground covered in vascular vegetation were associated with the probability of capturing single TB possums. A subsequent case control study was conducted at the larger scale of farms to identify possum habitat, geographic and farm factors associated with the probability of tuberculous possums being present on a farm, using random effects logistic regression modelling. A geographic information system (GIS) was used to combine and analyse digital geographic data sets to produce variables for the statistical analysis. Geographic data used for the study comprised farm boundaries, rivers, slope, and vegetation coverage which was derived from satellite imagery. Fragstats, a habitat pattern analysis programme, was used to produce variables representing patterns of possum habitat on farms. Variables were included in the model to account for spatial autocorrelation patterns in the data and a variogram analysis was conducted on residuals to test for any significant spatial autocorrelation that was not accounted for in the model. Distance from the coastal forest, total area of the farm, area of pine forest, area of manuka/pasture and proportion of the farm covered with podocarp/broadleaved species were the significant variables in the final model. An automated classification procedure was used to generate a vegetation map from a SPOT3 multi-spectral satellite image for this study. The resulting raster map comprised 8 vegetation classes, including 3 tree classes, 4 scrub classes and a pasture class. This map had a high degree of spatial accuracy and more detailed vegetation classes than existing digital data available in New Zealand. However, some categories contained a heterogeneous range of plant species which limited the discriminatory power of models using this vegetation data. Spatial clustering of percent TB-positive years, five-year cumulative incidence and space-time clustering of annual cumulative incidence of TB in cattle were analysed for beef breeding farms in the area in which the farm-level case control study was conducted. Analyses used the spatial and space-time scan statistics which are available in SaTScan, a cluster analysis software package. Mapping of farms included in the resulting clusters enabled us to develop hypotheses on the association of different habitat patterns with the resulting disease patterns in cattle. This study also highlighted the value of these cluster analysis techniques as tools in surveillance for wildlife TB, by using farm-level patterns of TB as an indicator of the possible presence of wildlife TB. The final stage of the project described in this thesis was the development of EpiMAN-TB, a spatial decision support system for the management of wildlife-related TB in New Zealand. Development of this tool was undertaken to assist the application of the research results described in this thesis at the field level. The system combined a relational database, map display tools, spatial analysis tools, simple expert systems and simulation models of the spread of TB in possums at the scale of individual farms and administrative regions. The software comprises five modules set within a user-friendly interface. The modules are: possum TB hot spot prediction, farm TB risk prediction, evaluation of possum control programmes at the farm and the regional level, and an administration module.