Please use this identifier to cite or link to this item:
|Title:||Costs and decisions in population management: koalas on Kangaroo Island|
|Citation:||MODSIM 2005 16th International Congress on Modelling and Simulation: Modelling and Simulation Society of Australia and New Zealand, December 2005 / Andre Zerger and Robert M. Argent (eds.): pp. 2082-2088|
|Publisher:||Modelling and Simulation Society of Australia and New Zealand|
|Publisher Place:||Canberra, Australia|
|Conference Name:||International Congress on Modelling and Simulation (16th : 2005 : Melbourne, Victoria)|
|Pollett, P.K. and J.V. Ross|
|Abstract:||Many populations have a negative impact on their habitat, or upon other species in the environment, if their numbers become too large. For this reason they are often managed using some form of control. The objective is to keep numbers at a sustainable level, while ensuring survival of the population. One such population is the koalas (Phascolarctos cinereus) of Kangaroo Island, South Australia. Between 1923 and 1925, 18 koalas were introduced to the island as a conservation measure to protect them (they were classified as a threatened species). Today, the Kangaroo Island koalas are considered to be a pest. Their overabundance has had a significant negative impact on the health of the Rough-barked Mannagum (Eucalyptus viminalis cygnetensis), along with other high-quality koala habitat. As a response to poor and insufficient habitat, numbers are predicted to decline sharply, and, because of the increased risk of extinction of the koalas and of other species, control and management programs have been proposed. Here we present models that allow population management programs to be assessed. Two common control regimes will be considered: reduction and suppression. Under the suppression regime the population is maintained close to a particular threshold through near continuous control, while under the reduction regime, control (for example culling or sterilisation) begins once the population reaches a certain threshold and continues until it falls below a lower pre-defined level. We discuss how to best choose the control parameters, and we provide tools that allow population managers to select reduction levels and control rates. Additional tools will be provided to assess the effect of different control regimes, in terms of population persistence and cost. In particular we consider the effects of each regime on the probability of extinction and the expected time to extinction, and compare the control methods in terms of the expected total cost of each regime over the life of the population. The usefulness of our results will be illustrated with reference to the control of the koala population. We select a suitable reduction level based on a specified probability of persistence, the genetic diversity of the population and the expected time between control phases. All are important in the management of native fauna. Firstly, while we are aiming to control the population, we wish to ensure the survival of the species without introducing risk additional to that faced prior to control. Next, genetic diversity, which is often overlooked when managing populations, is of utmost importance. The aim is to avoid inbreeding depression and to allow for evolutionary change. We must select a minimum reduction level that ensures a high probability of persistence, while maintaining an adequate level of genetic diversity. We find that a reduction level larger than that derived through these considerations will often be allowable in practice. To aid in selecting the reduction level, we also provide an explicit formula for the expected time between culling phases. Population managers can then select a reduction level so that the time between implementing successive controls is larger than some stipulated minimum (necessitated, for example, by resource constraints). The optimal rate of culling is then obtained by minimizing the cost of each culling phase, before finally selecting the optimal regime for control in terms of the expected cost of control over the life of the koala population. Our results can be easily extended to various control types (for example, sterilisation and translocation), and birth and death rates other than the ones considered here (for example, we may employ logistic birth rates). Consequently, we anticipate that our approach will be useful in a variety of population management contexts.|
|Rights:||Copyright status unknown|
|Appears in Collections:||Aurora harvest|
Mathematical Sciences publications
Files in This Item:
There are no files associated with this item.
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.