Habitat destruction and degradation are the most important threatening processes for the majority of species of conservation concern. As landscapes become more fragmented their spatial structure becomes increasingly important for the viability of wildlife populations. Therefore, understanding the dynamics of how populations interact with landscape structure is crucial for developing effective wildlife management and monitoring strategies. To achieve robust management, such an understanding should be incorporated into decision theory approaches for the design of management and monitoring strategies. Monitoring is a crucial component for assessing the outcome of management, because this can then be used to inform future management actions in an adaptive management framework. However, for many species, their spatial population dynamics are not well understood and objective management frameworks not well developed.
In this thesis I use modelling approaches to develop the components of an adaptive management framework for species of conservation concern that occur in fragmented landscapes. I use the koala (Phascolarctos cinereus) as a study organism and specifically address three key components that are important for adaptive management. These components are: (1) the development of novel approaches for modelling and understanding spatial population processes, (2) the application of modelling approaches to develop general principles and methods for managing wildlife populations in fragmented landscapes and (3) the application of modelling approaches to identify the best spatio-temporal sampling strategies for monitoring population trends in fragmented landscapes. This research advances our understanding of the dynamics of wildlife populations in fragmented landscapes, but importantly also develops tools and general principles for the adaptive management of these populations.
To improve our understanding of the dynamics of populations in fragmented landscapes I develop several novel modelling approaches. Firstly, I develop an approach for disassociating the effect of natural habitat and anthropogenic influences on population distributions using static occupancy models. This approach allows inferences to be made about how important the distribution of habitat is compared to other human-related factors. Secondly, I develop modelling approaches for understanding habitat selection and dispersal processes. In particular, I address common problems for modelling complex habitat selection processes and parameterising dispersal simulation models. The habitat selection models are novel because they account for habitat preferences that depend upon the spatial location of habitat. The novel emphasis for the dispersal models, is showing that pattern-oriented approaches are useful for parameterising dispersal simulation models, even when we have little data. These models make an important contribution to our understanding of and methods for understanding spatial population processes.
These and other modelling approaches are then applied to some key management questions, in order to develop general principles and methods for wildlife management decision-making. These studies relate to identifying the best spatial allocation of different management strategies, the planning of road networks to minimise impacts on wildlife and the impact of different mortality rates on the ranking of habitat protection strategies. First, I show that the relative spatial distribution of habitat and anthropogenic influences has important implications for the spatial allocation of management strategies. Second, I show that increasing traffic volume on existing roads is generally preferential to building new roads in terms of minimising the impact on wildlife mortality. Third, I use a multi-criteria decision analysis to show that the spatial arrangement of protected habitat is much less important for populations subject to high mortality rates than those subject to low mortality rates.
Finally, spatially-explicit modelling approaches are applied to questions related to how sampling effort should best be allocated spatially and spatio-temporally to monitor population trends. These studies reveal that the best sampling strategy depends predictably upon the monitoring objectives and the dynamics of the species. Using the principles developed, improved monitoring strategies and ultimately improved management strategies will be possible.
This thesis makes an important contribution to the development of adaptive management strategies for wildlife populations in fragmented landscapes. To implement an adaptive management approach, the modelling, management and monitoring components must be explicitly linked. By developing each of these components, this thesis provides a strong basis from which an adaptive management framework can be constructed. How this may be achieved and key areas for future research are discussed.