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In this study, key ecological modelling limitations of a process-based simulation model and a Bayesian network were reduced by combining the two approaches. We demonstrate the combined modelling approach with a case study investigating increases in woody vegetation density in northern Australia's tropical savannas. We found that by utilising the strengths of a simulation model and a Bayesian network we could both forecast future change in woody vegetation density and diagnose the reasons for current vegetation states. The local conditions of climate, soil characteristics and the starting population of trees were found to be more important in explaining the likelihood of change in woody vegetation density compared to management practices such as grazing pressure and fire regimes. We conclude that combining the strengths of a process and BN model allowed us to produce a simple model that utilised the ability of the process model to simulate ecosystem processes in detail and over long time periods, and the ability of the BN to capture uncertainty in ecosystem response and to conduct scenario, sensitivity and diagnostic analysis. The overall result was a model that has the potential to provide land managers with a better understanding of the behaviour of a complex ecosystem than simply utilising either modelling approach in isolation. [Copyright &y& Elsevier]

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]

Invasive wildlife often causes serious damage to the economy and agriculture as well as environmental, human and animal health. Habitat models can fill knowledge gaps about species distributions and assist planning to mitigate impacts. Yet, model accuracy and utility may be compromised by small study areas and limited integration of species ecology or temporal variability. Here we modelled seasonal habitat suitability for wild pigs, a widespread and harmful invader, in northern Australia. We developed a resource-based, spatially-explicit and regional-scale approach using Bayesian networks and spatial pattern suitability analysis. We integrated important ecological factors such as variability in environmental conditions, breeding requirements and home range movements. The habitat model was parameterized during a structured, iterative expert elicitation process and applied to a wet season and a dry season scenario. Model performance and uncertainty was evaluated against independent distributional data sets. Validation results showed that an expert-averaged model accurately predicted empirical wild pig presences in northern Australia for both seasonal scenarios. Model uncertainty was largely associated with different expert assumptions about wild pigs' resource-seeking home range movements. Habitat suitability varied considerably between seasons, retracting to resource-abundant rainforest, wetland and agricultural refuge areas during the dry season and expanding widely into surrounding grassland floodplains, savanna woodlands and coastal shrubs during the wet season. Overall, our model suggested that suitable wild pig habitat is less widely available in northern Australia than previously thought. Mapped results may be used to quantify impacts, assess risks, justify management investments and target control activities. Our methods are applicable to other wide-ranging species, especially in data-poor situations.