Using Spatio-Temporal modelling as a decision support tool for management of a native pest herbivore
Corvinus University of Budapest
Faculty of Computing, Health and Science
School of Natural Sciences
Landscape modification can alter the distribution and abundance of wildlife, which can result in irruptions of native species causing significant impacts on economically and ecologically valuable systems. This study investigated the applications of the Spatio-Temporal Animal Reduction (STAR) model, originally designed for the management of feral ungulates, by adapting it for the management of a native pest herbivore (the Tasmanian pademelon, Thylogale billardierii) within an agricultural-forest mosaic, typical of Tasmanian (Australian) agricultural landscapes. Empirical data of habitat and demographic features of a pest population were inputted into STAR to test the cost-effectiveness of three simulated density reduction models. Compared with the projected population growth under no management, simulations demonstrated that low, medium and high density reduction all reduced population abundance over 10 years. Cost increased with the level of population reduction due to increasing difficulty with locating individuals. The revenue gained from a simulated harvest was greatest for medium-intensity density reduction. We propose STAR can be used as a decision support tool to guide situations considering resource availability, browsing intensity and site-specific management objectives. The application of STAR highlights the model’s adaptability across diverse pest populations, landscape features and where there is competition for resources between domestic and native populations.
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