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Semiarid and arid woodlands across much of southern Australia have been subject to prolonged high total grazing pressure leading to loss of species diversity and a lack of recruitment of long-lived perennial species. Regeneration of perennial species requires grazing pressure to be maintained at low levels, but gaps remain in our ecological understanding of regeneration potential. The present study investigated the abundance of germinable seed in the soil seedbank in high quality remnants of Casuarina pauper F.Muell. ex L.A.S.Johnson (Belah) woodlands in north-west Victoria. Seed viability and response to six seed pretreatments including dry heat, wet heat, smoky water, soaking in distilled water, cold stratification and sand scarification were also tested to determine whether seed factors may be limiting regeneration. Results suggest that seed viability was not a limiting regeneration factor for nine out of the 10 perennial species tested with the exception of Alectryon oleifolius S.T. Reynolds. Only small amounts of seed from perennial species, largely Chenopodiaceae species and C. pauper, were found in the soil seedbanks of intact sites, suggesting component perennial species do not maintain long-term soil seedbanks. Results suggest that natural recovery of perennial species diversity in depauperate sites will depend on seed dispersal. [ABSTRACT FROM AUTHOR]

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 km2 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. [ABSTRACT FROM AUTHOR]