Using a Bayesian belief network to predict suitable habitat of an endangered mammal – The Julia Creek dunnart (Sminthopsis douglasi)

Wildlife managers are often required to make important conservation and recovery decisions despite incomplete and uncertain knowledge of the species and ecosystems they manage. Conducting further research to collect more empirical data may reduce that uncertainty. However, a sense of urgency often surrounds threatened or endangered species’ management and decisions cannot wait until a definitive understanding of a species’ ecology and distribution is obtained. Bayesian belief networks (BBNs) are proving to be valuable and flexible tools for integrating available expert knowledge and empirical data, thus strengthening conservation decisions when empirical data is lacking. We developed a BBN model and linked it to a geographical information system (GIS) to map habitat suitability for the Julia Creek dunnart (Sminthopsis douglasi), an endangered ground-dwelling mammal of the Mitchell grasslands of north-west Queensland, Australia. Expert knowledge, supported by field data, was used to determine the probabilistic influence of grazing pressure, density of the invasive shrub prickly acacia (Acacia nilotica), land tenure, soil variability and seasonal variability on dunnart habitat suitability. The model was then applied in a GIS to map the likelihood of suitable dunnart habitat. Sensitivity analysis was performed to identify the influence of environmental conditions and management options on habitat suitability. The study provides an example of how expert knowledge and limited empirical data can be combined within a BBN model, and linked to GIS data, to assist in recovery planning of endangered fauna populations. [Copyright &y& Elsevier]