Melaleuca densispicata in Currawinya National Park, South-West Queensland: Ecology and preliminary implications for management of a rare plant species

Melaleuca densispicata Byrnes is an uncommon species with a limited distribution, comprising disjunct populations in inland southern Queensland and northern New South Wales, Australia. It is a dense, woody shrub, 2–4 m in height, which exhibits a marked 'clumping' growth habit. It has thick, papery bark and displays many white flowers during spring or early summer. Although it has long been known to exist, M. densispicata was only formally described in 1984, and very little is currently known about its ecology or specific management requirements. There are only seven known subpopulations of the species across its range. A major population at the western limit of its distribution occurs on Currawinya National Park (28°52'S, 144°30'E). Here, it is locally abundant and listed as a noteworthy plant species under the Management Plan (Queensland Parks & Wildlife Service 2001). This study aimed to identify patterns in the distribution of M. densispicata in Currawinya National Park, describe its ecological niche and role, and provide management recommendations for the species within the study area. Recent anecdotal observations of recruitment failure in south-western Queensland (Peter McRae, QPWS, October 2004, pers. comm.; Dick O'Connell, local grazier, July 2005 pers. comm.) caused additional emphasis to be placed on the examination of recruitment and recruitment factors.

Optimal management of agricultural systems

To remain competitive, many agricultural systems are now being run along business lines. Systems methodologies are being incorporated, and here evolutionary computation is a valuable tool for identifying more profitable or sustainable solutions. However, agricultural models typically pose some of the more challenging problems for optimisation. This chapter outlines these problems, and then presents a series of three case studies demonstrating how they can be overcome in practice. Firstly, increasingly complex models of Australian livestock enterprises show that evolutionary computation is the only viable optimisation method for these large and difficult problems. On-going research is taking a notably efficient and robust variant, differential evolution, out into real-world systems. Next, models of cropping systems in Australia demonstrate the challenge of dealing with competing objectives, namely maximising farm profit whilst minimising resource degradation. Pareto methods are used to illustrate this trade-off, and these results have proved to be most useful for farm managers in this industry. Finally, land-use planning in the Netherlands demonstrates the size and spatial complexity of real-world problems. Here, GIS-based optimisation techniques are integrated with Pareto methods, producing better solutions which were acceptable to the competing organizations. These three studies all show that evolutionary computation remains the only feasible method for the optimisation of large, complex agricultural problems. An extra benefit is that the resultant population of candidate solutions illustrates trade-offs, and this leads to more informed discussions and better education of the industry decision-makers.