Modelling species distributional shifts across broad spatial extents by linking dynamic occupancy models with public‐based surveys

AIM: To understand climate and landscape drivers of species distributional shifts across broad spatial extents by integrating dynamic occupancy models with distribution data collected from the public. LOCATION: New South Wales (NSW), Australia. METHODS: We used data on koala (Phascolarctos cinereus) presence and absence collected across the state of NSW from public surveys between 1987 and 2011. A dynamic occupancy model was built to quantify the role of climate and land use change on koala extinction risk and occupancy. We contrasted the model results against the more usual static occupancy model approach. We then developed scenarios of future climate, land clearing and urbanization and predicted the distribution of the koalas over the next 20 years based on the dynamic occupancy model. RESULTS: The static model indicates koala occupancy in 1987 and in 2011 depended most strongly on annual rainfall and distance to water features. Housing density and its interaction with Eucalyptus forest cover only minimally affected koala occupancy. However, for the dynamic occupancy model, extinction risk (the metric of dominant concern for species conservation) depended most strongly on Eucalyptus forest cover and its interaction with housing density, while annual rainfall only minimally affected extinction risk. We predicted extinction risk to be higher in western NSW than in the east and that extinction risk may increase under future scenarios of climate and land use change. MAIN CONCLUSIONS: This study underlines the importance of incorporating extinction dynamics when modelling species distributional shifts under climate and land use change and we provide an approach for doing so using public‐based surveys. As conservation objectives usually aim to maximize persistence, this is likely to lead to more reliable identification of conservation priorities than using static species distribution models. Combining public‐based surveys and dynamic occupancy models provides a powerful approach for achieving this across broad spatial extents, thus providing an alternative approach when field‐based data collection is impractical.