Prediction of the spatial distribution and relative abundance of ground-dwelling mammals using remote sensing imagery and simulation models

Abstract

We present an approach that allows current, retrospective and future relative abundances of mammal species to be predicted across landscapes. A spatial generalized regression model of species relative abundance based on habitat quality and time since disturbance was combined with coverages of the spatial distribution of habitat quality derived from a simulation model which predicts the historical and future spatial arrangement of forest habitat. The strength of this approach is that the input habitat data can be derived as part of a standard forest inventory mapping program with the addition of high spatial resolution remote sensing imagery. Furthermore, it operates at the scale used for wildlife management in Australia, which makes it widely applicable. To demonstrate the approach we use data collected over 20 years on the long-nosed potoroo (Potorous tridactylus) and the large wallabies (red-necked wallaby, Macropus rufogriseus, and swamp wallaby, Wallabia bicolor) and their habitats following wildfire. Results indicate the relative abundance of the potoroo has increased, from initially sparse numbers of less than 0.5 % of plot-night occurrences to close to 3% approximately twenty years after a major fire event. The large wallabies by contrast decreased in relative abundance from about 20% since the major fire event. Presently the relative abundance of large wallabies was modelled at 2% of plot-nights with tracks which was very low. Predictions of future relative abundance without additional disturbance were low, with the region likely to be unsuitable for the species in the next 5 years. These models offer tools for investigating the current and historical abundances of key species which can provide data to forest managers for wildlife management thereby translating current scientific understanding into tools suitable for every-day use by forest managers.