Using trap data the Hawdon, Poulter and South Branch valleys, a spatial distribution model was created for Stoats (Mustela erminea), Ship Rats (Rattus rattus) and Weasels (Mustela nivalis) in the North Branch of the Hurunui River. Ten spatial attributes were analysed in this thesis as potential spatial predictors of Stoats, Ship rats or Weasels; four of which were distance related measurements (distance from ecotonal edge, distance from river, distance from river tributary and distance from trapping edge); three were climate based variables (mean maximum temperature, mean minimum temperature and mean precipitation) and three were topographical based variables (elevation, aspect and slope). Relationships that existed between each spatial attribute and the number of Stoats, Ship Rats and Weasels caught were quantified by comparing the significance of the mean trapping rate with each spatial attribute and expressed spatially as maps in a Geographical Information System (GIS). Results from this thesis found elevation, aspect and distance from ecotonal edge as potential spatial predictors of Stoat populations. Elevation and aspect were found to be potential predictors of Ship rat and Weasel populations. GIS is able to predict the spatial distribution of pest species to a similar (or better) level compared to more formal associative models. The potential of GIS is however, restrained by the same limitations associated with these models. By using a larger trapping data set and identifying a number of social interactions between Stoats, Ship Rats and Weasels, one can improve the accuracy of spatially modelling each species within a Beech forest environment. Therefore, improve our understanding how landscapes influence the distribution of each pest species.

Using trap data the Hawdon, Poulter and South Branch valleys, a spatial distribution model was created for Stoats (Mustela erminea), Ship Rats (Rattus rattus) and Weasels (Mustela nivalis) in the North Branch of the Hurunui River. Ten spatial attributes were analysed in this thesis as potential spatial predictors of Stoats, Ship rats or Weasels; four of which were distance related measurements (distance from ecotonal edge, distance from river, distance from river tributary and distance from trapping edge); three were climate based variables (mean maximum temperature, mean minimum temperature and mean precipitation) and three were topographical based variables (elevation, aspect and slope). Relationships that existed between each spatial attribute and the number of Stoats, Ship Rats and Weasels caught were quantified by comparing the significance of the mean trapping rate with each spatial attribute and expressed spatially as maps in a Geographical Information System (GIS). Results from this thesis found elevation, aspect and distance from ecotonal edge as potential spatial predictors of Stoat populations. Elevation and aspect were found to be potential predictors of Ship rat and Weasel populations. GIS is able to predict the spatial distribution of pest species to a similar (or better) level compared to more formal associative models. The potential of GIS is however, restrained by the same limitations associated with these models. By using a larger trapping data set and identifying a number of social interactions between Stoats, Ship Rats and Weasels, one can improve the accuracy of spatially modelling each species within a Beech forest environment. Therefore, improve our understanding how landscapes influence the distribution of each pest species.