Date of Award
Doctor of Philosophy
School of Natural Sciences
Faculty of Health, Engineering and Science
Dr Robert Davis
Dr Eddie van Etten
Professor Pierre Horwitz
Biodiversity conservation throughout the world is challenged by the impacts of a changing climate on fragmented landscapes. To mitigate these threats, conservation managers require models which can demonstrate the consequences of both negative impacts and management actions. This need can be addressed through spatial modelling applications. Unfortunately, throughout much of the world, spatial modelling is forgone, being seen as requiring skills and resources beyond the means of many conservation planners and managers. This thesis seeks to address this dilemma by delivering criteria for a successful modelling application and by providing case studies which demonstrate how appropriate modelling can be undertaken without highly specialised skills or prohibitively expensive software and equipment. In this way it facilitates the delivery of better targeted and, consequently more effective, management actions.
For my case studies I have used the south-western corner of Australia as a demonstration landscape. This region is recognised internationally as a “biodiversity hotspot,” not only for the biological richness and uniqueness of species but also for the level of threat to which they are subject. Like many landscapes throughout the world, much or this region’s natural biota exists in fragmented, fragile and degraded patches and is therefore highly vulnerable to the anticipated impacts of anthropogenic global warming.
In this thesis I have: 1) examined the principles of spatial modelling and reviewed how spatial modelling has been applied to conservation management in this region, 2) conducted examples of different forms of spatial modelling using actual regional conservation management issues, and 3) demonstrated how these examples can be incorporated into conservation management planning.
My key findings are:
- Spatial modelling provides users with an opportunity to effectively test hypotheses, thereby informing the planning process and improving conservation outcomes. Where spatial modelling is omitted from the process, knowledge gaps are often addressed by the axiomatic and by assumption. This is contrary to the principles of effective adaptive management.
- Modelling tools are inherently more effective when selected for their capacity to meet a planning objective rather than where projects are tailored to meet a model’s capacity.
- The coordinated use of multiple tools can often provide a more robust understanding of the consequences impacts and mitigating actions.
- All tools and data sets used should be utilised with a clear and acknowledged understanding of their suitability, strengths and limitations.
- A wide range of spatial modelling tools (and data sets) are freely and readily available to conservation managers. Most of these come with excellent tutorials and support services.
- Data gaps can often be addressed through targeted field observations, obtained through complimentary planning processes, or synthesised from accessible data sets.
- There is a very large body of peer reviewed literature demonstrating means by which others have applied existing modelling tools, or developed tools themselves, to meet a wide range of applications. Accessing this literature is an excellent means of building spatial modelling capacity.
- New and improved tools, methodologies and data sets are constantly being developed.
- A failure to implement effective spatial modelling is becoming increasing difficult to justify.
Molloy, S. (2013). Applying the principles of spatial modelling to the management of biodiversity in the fragmented landscapes of south-western Australia. https://ro.ecu.edu.au/theses/870
2. Common bronzewing probability tool.xlsx (61 kB)
3. Golden whistler probability tool.xlsx (62 kB)
4. Red winged fairywren probability tool.xlsx (59 kB)
5. Striated Pardalote probability tool.xlsx (63 kB)
6. Western gergone probability tool.xlsx (62 kB)
7. Western rosella probability tool.xlsx (61 kB)
8. Western spinebill probability tool.xlsx (58 kB)
9. White breasted robin probability tool.xlsx (61 kB)
10. White browed scrubwren probability tool.xlsx (62 kB)
Ch.4.1 2287 Kernel analysis and movements.pdf (3127 kB)
Ch.4.2 6394 Kernel analysis and movements.pdf (3135 kB)
Ch.4.3 9591 Kernel analysis and movements.pdf (3116 kB)
Ch.4.4 10624 Kernel analysis and movements.pdf (3138 kB)
Ch.4.5 10626 Kernal analysis and movements.pdf (3138 kB)
Ch.4.6 11669 Kernel analysis and movements.pdf (3136 kB)
Ch.4.7 11691 Kernel analysis and movements.pdf (3111 kB)
Ch.4.8 14577 Kernel analysis and movements.pdf (3135 kB)
Ch.4.9 14884 Kernel analysis and movements.pdf (3142 kB)
Ch.4.10 24169 Kernal analysis and movements.pdf (3136 kB)
Ch.4.11 24878 Kernel analysis and movements.pdf (3145 kB)
Ch.4.12 25673 Kernel analysis and movements.pdf (3134 kB)
Ch.4.13 28403 Kernel analysis and movements.pdf (3136 kB)
Ch.4.14 47088 Kernel analysis and movements.pdf (3149 kB)
Ch.4.15 51171 Kernel analysis and movements.pdf (3136 kB)
Ch.4.16 C1-11539 Kernel analysis and movements,.pdf (3136 kB)
Ch.4.17 C2-10000 Kernel analysis and movements.pdf (3113 kB)
Ch.4.18 C3-27762 Kernel analysis and movements.pdf (3145 kB)
Ch.4.19 C4-14162 Kernel analysis and movements.pdf (3150 kB)
Ch.4.20 C5-24328 Kernel analysis and movements.pdf (3123 kB)
Ch.4.21 C6-29371 Kernal analysis and movements.pdf (3158 kB)
Ch.4.22 C7-1004 Kernel analysis and movements.pdf (3140 kB)
Ch.4.23 T1-8648 Kernel analysis and movements.pdf (3122 kB)
Ch.4.24 T2-28836 Kernel analysis and movements.pdf (3106 kB)
Ch.4.25 T3-442 Kernel analysis and movements.pdf (3138 kB)
Ch.4.26 T4-10340 Kernel analysis and movements.pdf (3124 kB)