Modelling the Spatial and Temporal Dynamics of Kangaroo Populations for Harvest Management: Final Report to the Department of Environment and Heritage, Canberra
For the past three years, university researchers (University of Queensland and University of New England), kangaroo managers (Queensland, New South Wales, South Australia and the Commonwealth) and Packer Tanning have been collaborating on a research project aimed at improving kangaroo management. The project has three broad aims: - Predict kangaroo numbers using rainfall or satellite imagery and other environmental data - Indirectly monitor kangaroo numbers and harvest rate using harvest statistics (e.g. sex ratio, carcass weight, catch-per-unit-effort) - Optimise survey methods, frequency and design The work has involved collating over 20 years of data in three states on kangaroo density from aerial surveys, harvest offtake, satellite imagery (greenness index or NDVI) and rainfall. Such a long time series of data covering vast areas has enabled models to be developed that should lead to improved kangaroo management. These models can be used to predict future kangaroo numbers, which should enable the frequency and intensity of expensive aerial surveys to be reduced. Better prediction of kangaroo distribution within management zones should also help quota and tag allocation. Rainfall and pasture conditions obviously influence changes in kangaroo numbers, but the relationships needed to be quantified. The sex ratio and average weight of carcasses vary regionally, for a variety of reasons. Most usefully for managers, these statistics track kangaroo density or harvest rate in some cases. Both harvest statistics and satellite imagery have the advantage of being regularly updated and a high spatial resolution, both shortcomings of broad-scale aerial survey. Aerial surveys have been conducted annually in the eastern states, which may not be the most efficient survey frequency. The optimal frequency can be identified by considering the risks of the population dropping to low density or rising to high density. These risks can be considered as costs to the kangaroo industry, graziers and to conservation, which must then be balanced. Risk can be reduced by increasing survey frequency or intensity, which is a cost to management, or reducing harvest rate, which is a cost to industry. In more arid, uncertain environments, regular surveys are required. However, in many of these areas, harvests are low and a reduced harvest rate is unlikely to be a cost to industry. The data also suggest a greater influence of movement on red kangaroo population dynamics than previously thought, with large areas experiencing rates of increase much higher than possible through birth and survival alone. This suggests movement needs to be considered when forecasting kangaroo density even at a regional (>10,000 square km) scale
