Animal movements in fire-prone landscapes.

Movement is a trait of fundamental importance in ecosystems subject to frequent disturbances, such as fire-prone ecosystems. Despite this, the role of movement in facilitating responses to fire has received little attention. Herein, we consider how animal movement interacts with fire history to shape species distributions. We consider how fire affects movement between habitat patches of differing fire histories that occur across a range of spatial and temporal scales, from daily foraging bouts to infrequent dispersal events, and annual migrations. We review animal movements in response to the immediate and abrupt impacts of fire, and the longer-term successional changes that fires set in train. We discuss how the novel threats of altered fire regimes, landscape fragmentation, and invasive species result in suboptimal movements that drive populations downwards. We then outline the types of data needed to study animal movements in relation to fire and novel threats, to hasten the integration of movement ecology and fire ecology. We conclude by outlining a research agenda for the integration of movement ecology and fire ecology by identifying key research questions that emerge from our synthesis of animal movements in fire-prone ecosystems.

Historical Maps from Modern Images: Using Remote Sensing to Model and Map Century-Long Vegetation Change in a Fire-Prone Region.

Understanding the age structure of vegetation is important for effective land management, especially in fire-prone landscapes where the effects of fire can persist for decades and centuries. In many parts of the world, such information is limited due to an inability to map disturbance histories before the availability of satellite images (~1972). Here, we describe a method for creating a spatial model of the age structure of canopy species that established pre-1972. We built predictive neural network models based on remotely sensed data and ecological field survey data. These models determined the relationship between sites of known fire age and remotely sensed data. The predictive model was applied across a 104,000 km(2) study region in semi-arid Australia to create a spatial model of vegetation age structure, which is primarily the result of stand-replacing fires which occurred before 1972. An assessment of the predictive capacity of the model using independent validation data showed a significant correlation (rs = 0.64) between predicted and known age at test sites. Application of the model provides valuable insights into the distribution of vegetation age-classes and fire history in the study region. This is a relatively straightforward method which uses widely available data sources that can be applied in other regions to predict age-class distribution beyond the limits imposed by satellite imagery.