Vegetation Dynamics in Coastal Heathlands of the Sydney Basin

David A. Keith, Mark G. Tozer

Abstract


Heathlands are dynamic ecosystems that change in response to fire regimes and climate variations, as well as endogenous processes such as competition between component species. An understanding of how heathlands change through time is central to the development of management strategies that aim to conserve them and maintain coexistence of their plants and animals. We briefly review the development of this understanding for Sydney’s coastal heathlands from the emergence of the first published work in the 1930s. In our previous work, we focussed on fire regimes and interspecific competition between plants as important processes that drive ecosystem dynamics (succession) and mediate species coexistence and diversity. Here, we synthesise our understanding of heathland dynamics into a state and transition framework. We first develop a simple classification of heathland states based on their composition of plant functional types and developmental stage with time since fire. We then propose a qualitative model that predicts transitions between states conditional upon intervals between fires, fire-mediated life cycle processes of component plant species and interactions between species. We applied the model to predict qualitative changes in heathland state under contrasting fire regime scenarios, and tested example predictions using a long-term study of heathland dynamics in Royal National Park. Empirical observations of overstorey and understorey change were generally consistent with model predictions, subject to variability between sites. Importantly, the model helps to identify fire scenarios that promote dynamic coexistence of multiple heathland states that each support different components of heathland biota. We conclude that simple process models can be very useful for informing management decisions by describing expected responses to alternative management strategies. These predictions lend themselves to testing in adaptive management experiments that seek to spread risks and improve understanding of ecosystem dynamics for future management.

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