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Land managers are often faced with a dilemma when trying to reconcile the trade-off between burning for fuel reduction and the values of biodiversity (Gill & Bradstock 1994). The occurrence of fire at short intervals helps to reduce the fuel load with benefits for fire suppression, but such a fire regime may threaten plant species that experience a period during which they are not reproductively mature and are killed by fire (Gill & Bradstock 1994; Bradstock et al. 1996, 1998). Despite considerable information about the response of species to fire regimes (Hoffman 1998; Russell-Smith et al. 1998), there are few scientific studies that offer comparative insight into the consequences of differing strategies of prescribed burning.
Prescriptions for the use of fire for conservation management are usually expressed in terms of the time since the last fire, as illustrated by studies of Banksia species in Australia. Gill & McMahon (1986) suggested that the minimum interval between fires in populations of the Australian shrub B. ornata should be 16 years to allow the population to replace itself, based on seed production, germination and survival of seedlings. Enright, Lamont & Marsula (1996) used a deterministic model to determine a fire management strategy to maximize the finite rate of population increase of B. hookeriana. Burgman & Lamont (1992) used a stochastic simulation model to consider management strategies for minimizing the risk of extinction and maximizing the mean population size of a B. cuneata population in the presence of unplanned fires. Similarly, Bradstock et al. (1996) used a simulation model to examine how different fire regimes influenced the persistence of a hypothetical serotinous Banksia species. These different approaches to determining the effect of fire on Banksia species indicate that the populations should be burnt at a particular age, but do not suggest whether the optimal strategy should also depend on other aspects of the population, such as abundance. These studies ignore the possibility that the optimal strategy might depend on the abundance of the species concerned, despite rarity being considered a critical factor in extinction events (Burgman, Ferson & Akçakaya 1993). By using a state-dependent decision-making tool, we can determine whether the optimal management strategy also depends on population size.
The purpose of this study was to use SDP to identify optimal fire management strategies for plant populations using B. ornata F. Muell. as an example. We also illustrated the use of SDP for reconciling the trade-off between fuel reduction burning and species persistence. Banksia ornata was chosen because there are good data on its fire ecology. Banksia ornata is a shrub that is relatively common in woodlands and mallee scrubs of south-eastern South Australia and western Victoria. It is a serotinous seeder, with individual plants killed by fire and local persistence dependent on successful germination from the canopy-stored seed bank (Specht, Rayson & Jackman 1958). Thus, B. ornata occurs predominantly as even-aged stands, and few if any viable seeds occur in the soil. Individuals do not begin to produce seed until approximately 5 years of age, and they survive for up to approximately 50 years (Gill & McMahon 1986). Populations will become locally extinct if fire does not occur prior to the death of all adult plants, but successive fires at short intervals will also lead to extirpation (local extinction) because the number of seeds produced by young plants is insufficient to regenerate the population. Although B. ornata is an abundant species, it is ecologically important and is likely to be indicative of fire management issues for numerous other obligate seeding species (Gill & Bradstock 1995). Gill & McMahon (1986) regarded B. ornata as a potentially useful indicator species because it is easily identified, dominant, fire-sensitive and its source of regeneration (seeds) is visible on the plant.
A model of the population dynamics of B. ornata was developed, and then used in conjunction with SDP to determine optimal fire management strategies that depended on the abundance and age of the population. The results of the SDP model were then compared with results obtained from age-dependent strategies where fire management was applied regardless of the abundance of the population.
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The method used in this paper determined the optimal fire management strategy for B. ornata by maximizing the expected number of years during which the species would persist in the next century. Other authors have attempted to determine optimal fire regimes of Banksia species using different methods and with different objectives. Enright, Lamont & Marsula (1996) estimated the optimal fire frequency for B. hookeriana, a serotinous fire-killed shrub of western Australia, by determining the fire interval that maximized the finite rate of population increase. Focusing on the finite rate of population increase ignores risks associated with prescribed fires, such as the chance of germination failure after a fire or the chance of a subsequent fire killing immature plants. When risks are appreciable, the optimal management strategy is to attempt to burn the population as infrequently as possible but before the plants senesce and density becomes low (Fig. 2). The optimal age of burning varied between 12 and 52 years depending on abundance (Fig. 2). However, the influence of considering population size was small compared with the influence of considering population age (Figs 3 and 4), suggesting that the age of the population rather than abundance was the most important management variable for B. ornata. Nevertheless, changing from an age-dependent strategy to one based on age and abundance (the SDP solution in Figs 3 and 4) could lead to an approximate halving of the 100-year extinction risk (Figs 3 and 4). Burgman & Lamont (1992) considered the efficacy of a number of different management strategies with a stochastic simulation model of B. cuneata. They determined that burning the population increased the risk of extinction, but it was not clear whether this recommendation may change depending on the population size. The size of the state space meant that an optimal state-dependent strategy could not be determined from their simulations. The results of our SDP model of B. ornata suggested that population size was less important than time since fire, although this may not be true for less abundant species such as B. cuneata, which may be exposed to greater stochasticity associated with small population size. SDP has the advantage over these other methods by incorporating stochasticity in the model while at the same time considering the state of the system. SDP also finds the true optimum with numerical methods, while simulation models require exhaustive analysis of all possible management regimes. However, current limitations of most computers mean that only a few thousand different states can be considered within an SDP model, often requiring somewhat simplified descriptions of population dynamics like those presented here.
The optimal strategy for the persistence of B. ornata is to burn the population at moderately regular intervals. However, to better mimic natural disturbance regimes and to promote ecosystem heterogeneity and diversity, several authors have argued that prescribed disturbances should have a random component (Keith & Bradstock 1994; McCarthy & Burgman 1995; Morrison et al. 1995). Richards, Possingham & Tizard (1999) used SDP to determine the optimal fire management strategy to retain a diversity of different successional states across a mosaic of patches. In such cases, although the mean interval is the same for all parts of the landscape, a diversity of fire intervals was required, with the prescription depending on the proportion of the area in early, mid- and late successional states.
In the solutions where younger stands of B. ornata were preferred to older stands (Fig. 2), the benefit of having young B. ornata was five times that of having old B. ornata. This suggests a very strong preference for younger stands at the expense of a higher risk of local extinction. The values we used were for illustrative purposes, and the exact values would depend on management priorities. In areas close to human assets, the relative benefit of young stands may be quite large. In more remote areas, the benefit may be negligible. Nevertheless, the B. ornata example illustrates how to consider the trade-offs between fuel reduction burning, control of unplanned fires and biodiversity conservation. It emphasizes that, as we do not have perfect control of fires, it is important to consider the effects of such unplanned events when making management decisions. In our example, costs of lighting and fighting fires were greatly simplified, but extra detail could be readily incorporated if the costs could be expressed in terms of the local persistence of the species. In the case of an endangered species, these relative costs are likely to be small and may have little influence on the results. However, for a common species like B. ornata, including management costs may mean that the do-nothing option is optimal for certain states of the population, especially in cases where the relative benefits of the two active management strategies (light a prescribed fire or control fires) are small.
Our model of B. ornata considered only a single population, ignoring dispersal of seeds between areas. The model predicts that a single population is destined for extinction in the presence of unplanned fire because a fire will eventually occur during its juvenile period. In reality, such local extinction events are usually balanced by dispersal of seeds into unoccupied areas. Such colonization events will buffer the species from global extinction. Including this feature would improve the realism of the model and might be included by using a metapopulation model or some other representation of space (Husband & Barret 1996; Bradstock et al. 1996, 1998; McCarthy, Gill & Lindenmayer 1999). Such spatial considerations may influence the optimal management strategy because the risk of unplanned fires may be greatly reduced by burning only a portion of the area.
We caution against the use of the present results as a recipe for management. Our results should be considered to be indicative and illustrative only at this stage. In particular, the strategy of burning relatively young (but reproductively mature) populations when densities are low depends on the exact density at which local extinction is deemed to occur, and the rate of decline in abundance of the plants. Slower rates of decline to local extinction, perhaps due to density-dependent effects on mortality, would result in an older optimal age of burning for these stands.
SDP is a new technique in a growing toolkit of methods that includes expert systems (Walker, Davies & Gill 1985), demographic prediction based on fire interval (Bradstock & O’Connell 1988), decision support systems based on monitoring of plant communities (Gill & Nicholls 1989), and methods based on stochastic interval distributions of fires considered concurrently with life-history characteristics of species (Gill & McCarthy 1998). The SDP model developed here explicitly considers population size and disturbance probability while implicitly considering the stochastic effects on other life-history processes. It differs from all other methods in optimizing management outcomes by being able to choose between projected pathways of the population in order to maximize the persistence of the species.
The SDP solution of the B. ornata model indicated that the optimal management strategy is state-dependent, with the most important state variable being the age of the population. Thus, optimal management requires that the state of the system be monitored if appropriate fire regimes are to be prescribed.