Australian Journal of Botany Australian Journal of Botany Society
Southern hemisphere botanical ecosystems
RESEARCH ARTICLE

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

Michael F. Clarke A D , Sarah C. Avitabile A , Lauren Brown A , Kate E. Callister A , Angie Haslem A B , Greg J. Holland B , Luke T. Kelly B , Sally A. Kenny C , Dale G. Nimmo B , Lisa M. Spence-Bailey A , Rick S. Taylor A , Simon J. Watson B and Andrew F. Bennett B

A Department of Zoology, La Trobe University, Bundoora, Vic. 3086, Australia.

B School of Life and Environmental Sciences, Deakin University, Burwood, Vic. 3125, Australia.

C Department of Botany, La Trobe University, Bundoora, Vic. 3086, Australia.

D Corresponding author. Email: m.clarke@latrobe.edu.au

Australian Journal of Botany 58(5) 363-372 https://doi.org/10.1071/BT10051
Submitted: 19 February 2010  Accepted: 14 May 2010   Published: 21 July 2010

Abstract

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 < 0.001, n = 88), confirming the utility of this method for ageing stands of mallee eucalypt vegetation. Validation data suggest the models provide a conservative estimate of the age of a site (i.e. they may under-estimate the minimum age of sites >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.


Acknowledgements

We gratefully acknowledge funding and support for this project from Parks Victoria, Department of Sustainability and Environment (Vic.), Mallee Catchment Management Authority, NSW National Parks and Wildlife Service, Department of Environment and Climate Change (NSW), Lower Murray–Darling Catchment Management Authority, Department for Environment and Heritage (SA), Land and Water Australia, Natural Heritage Trust, Birds Australia (Gluepot Reserve), Australian Wildlife Conservancy (Scotia Sanctuary) and the Murray Mallee Partnership. We are grateful to the University of Ballorat and to the Doyle and Barnes families for granting access to Nanya, Petro and Lethero Stations, respectively. Thanks go to Lauren Fraser for assistance with references and many volunteers who assisted with vegetation surveys and to Martin Westbrooke and Simon Cook who directed us to many of the validation sites. We are also very grateful for the constructive suggestions of two anonymous referees.


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Appendix 1.  Coefficient estimates and standard errors (s.e.) for models of the change in mean stem diameter (cm) per site in relation to time since fire for six species of mallee eucalypts in the Murray Mallee region
Models are based on data from sites of known time since fire ranging from 0 to 94 years. Alternative models are presented for which time (years) is modelled in linear form and square-root transformed (sqr). D2 = the percentage of deviance explained by the model
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Appendix 2.  Coefficient estimates and standard errors (s.e.) for models of the change in mean stem diameter (cm) per site in relation to time since fire for mallee eucalypts in the Murray Mallee region
Models are based on data recorded from all mallee eucalypts at a site, regardless of species. Models are presented for datasets based on sites of <35 years since fire and 0–94 years since fire, respectively. In each case, alternative models are presented for which time (years) is modelled in linear form and square-root transformed (sqr). D2 values represent the percentage of deviance explained by the overall model
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Appendix 3.  Relationship between the predictions of two methods of generating time-since-fire predictions by using (1) the average across species of the best linear or square-root species-specific models from the combined dataset of 0–94 years (x-axis) and (2) the average across-species time-since-fire predictions from the linear species-specific models built only on 0–35-year data (y-axis) (Pearson’s correlation (r) = 0.94, P < 0.001). The solid line depicts where a 1 : 1 correspondence between predicted ages of sites determined by each model would fall.
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