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Over-abundant
or pest species are believed to be responsible for the massive
disruption of ecological communities and for the decline and
extinction of a wide range of native species. Many agencies
and organisations including Federal, State and Local governments
commit significant resources managing over-abundant species
annually (Reddiex et al. 2004). However, there is limited
evidence that management has led to a reduction in threats
generated by over-abundant species or to a reversal in the declines
of vulnerable species and communities (eg. Hone 1994).
Monitoring
data play a central role in informing the decisions of management
agencies and is critical for evaluating the ecological and economic
costs and benefits of on-ground management actions such as pest
animal control. It is imperative, therefore, that monitoring
data be sufficient to enable informed decision-making.
Previous work
on optimal monitoring has focused on the issue of statistical power
and has ignored the costs and benefits of using information to
determine the best management decision. Another option is to place
the monitoring inside the decision theory problem (Possingham
et al. 2001) and ask what monitoring plan is needed to
make the best management decision, taking into account the cost of
monitoring.
The aim of my
project is to devise tools for detecting change in pest animal
populations and the threatened species being protected, reliably
and cost effectively, and placing monitoring within a robust
management framework. Based on existing datasets on overabundant
species I aim to develop new theory and mathematical methods to
incorporate optimal monitoring scenarios and economic information
into a decision theory approach for the management of pest species
in Australia.
This idea of
integrating monitoring into a decision theory approach for
management is a relatively new innovation in conservation
management (see Field et al. 2004), and therefore provides
a unique opportunity for the application of this work to both the
management of pests in other countries, for example New Zealand,
and utilisation for other management scenarios.
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