Rick S. Taylor

Ageing mallee eucalypt vegetation after fire: insights for successional trajectories in semi-arid mallee ecosystems

A critical requirement in the ecological management of fire is knowledge of the age-class distribution of the vegetation. Such knowledge is important because it underpins the distribution of ecological features important to plants and animals including retreat sites, food sources and foraging microhabitats. However, in many regions, knowledge of the age-class distribution of vegetation is severely constrained by the limited data available on fire history. Much fire-history mapping is restricted to post-1972 fires, following satellite imagery becoming widely available. To investigate fire history in the semi-arid Murray Mallee region in southern Australia, we developed regression models for six species of mallee eucalypt (Eucalyptus oleosa F.Muell. ex. Miq. subsp. oleosa, E. leptophylla F.Muell. ex. Miq., E. dumosa J. Oxley, E. costata subsp. murrayana L. A. S. Johnson & K. D. Hill, E. gracilis F.Muell. and E. socialis F.Muell. ex. Miq.) to quantify the relationship between mean stem diameter and stem age (indicated by fire-year) at sites of known time since fire. We then used these models to predict mean stem age, and thus infer fire-year, for sites where the time since fire was not known. Validation of the models with independent data revealed a highly significant correlation between the actual and predicted time since fire (r = 0.71, P  35 years since fire). Nevertheless, this approach enables examination of post-fire chronosequences in semi-arid mallee ecosystems to be extended from 35 years post-fire to over 100 years. The predicted ages identified for mallee stands imply a need for redefining what is meant by ‘old-growth’ mallee, and challenges current perceptions of an over-abundance of ‘long-unburnt’ mallee vegetation. Given the strong influence of fire on semi-arid mallee vegetation, this approach offers the potential for a better understanding of long-term successional dynamics and the status of biota in an ecosystem that encompasses more than 250 000 km2 of southern Australia.

Fire Mosaics and Reptile Conservation in a Fire-Prone Region

Fire influences the distribution of fauna in terrestrial biomes throughout the world. Use of fire to achieve a mosaic of vegetation in different stages of succession after burning (i.e., patch-mosaic burning) is a dominant conservation practice in many regions. Despite this, knowledge of how the spatial attributes of vegetation mosaics created by fire affect fauna is extremely scarce, and it is unclear what kind of mosaic land managers should aim to achieve. We selected 28 landscapes (each 12.6 km(2) ) that varied in the spatial extent and diversity of vegetation succession after fire in a 104,000 km(2) area in the semiarid region of southeastern Australia. We surveyed for reptiles at 280 sites nested within the 28 landscapes. The landscape-level occurrence of 9 of the 22 species modeled was associated with the spatial extent of vegetation age classes created by fire. Biogeographic context and the extent of a vegetation type influenced 7 and 4 species, respectively. No species were associated with the diversity of vegetation ages within a landscape. Negative relations between reptile occurrence and both extent of recently burned vegetation (≤10 years postfire, n = 6) and long unburned vegetation (>35 years postfire, n = 4) suggested that a coarse-grained mosaic of areas (e.g. >1000 ha) of midsuccessional vegetation (11-35 years postfire) may support the fire-sensitive reptile species we modeled. This age class coincides with a peak in spinifex cover, a keystone structure for reptiles in semiarid and arid Australia. Maintaining over the long term a coarse-grained mosaic of large areas of midsuccessional vegetation in mallee ecosystems will need to be balanced against the short-term negative effects of large fires on many reptile species and a documented preference by species from other taxonomic groups, particularly birds, for older vegetation.

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 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.