The purpose of this plan is to provide strategic direction for investment in managing the ACT’s natural resources. In responding to the threats facing our natural assets, governments and community are willing to substantially invest in their repair and protection. These investments need to be spent in the right places and on the most pressing needs. Actions need to be logically connected to outcomes and the plan must be supported by the community.

This plan forms the basis for investment by government, business and community in addressing these issues.

The underlying basis of this plan is a framework known as ‘adaptive management’ or learning from doing – adaptive managers learn by implementing plans and policies (Allan 2007). The process can take place at the small scale where one person provides both experimental and governing sides of the process; up to the large scale where government agencies and other organisations provide the input and assessment.

Adaptive management is used when outcomes of actions cannot be accurately predicted so that there is an element of uncertainty about the best management interventions to use. Such uncertainty can arise through natural variability (e.g. weather or climate) or from an incomplete knowledge of systems and how they work. Waiting until the knowledge base is more complete is not a tenable option when it is clear that ecosystem function is declining and the community is willing for action to take place.

Under an adaptive management approach, hypotheses are drawn and action takes place in a systematic fashion so that outcomes can supply knowledge about the ecosystem and its responses. These outcomes then supply knowledge for the next cycle of actions. The resulting series of steps through planning, implementation, monitoring and assessment (see Figure 4) is known as passive adaptive management. In a more active approach and one that is more likely to result in successful outcomes, implementation occurs as large-scale experiments that are testing specific hypotheses about responses, thus enhancing performance of each
cycle.

First attempts to specifically describe targets and relationships may look clumsy in 20 years’ time, but it is the only way to make underlying assumptions visible, test them and improve on them.