Using integrated data analysis to assess regional transferability

Project start date: 18/07/2022
Project end date: 31/03/2023
NESP funding: $139,984 (GST-exclusive)

Hub researchers have created integrated datasets which demonstrate social and ecological similarities between the regions analysed. These datasets were then visualised as a series of simple-to-use maps showing regions that share similar characteristics and are therefore likely to support similar conservation and land-management interventions. The maps are presented alongside tables which describe core social, ecological and interactive variables for different regions.

The report also includes a decision-making tree to assist land managers in determining best-practice conservation actions. These outcomes will help to support biodiversity and ecosystems across Australia and increase the potential impact of conservation and land-management research.

Read more in the final report.

A total of 424 variables were used to describe each of Australia’s 56 Natural Resources Management (NRM) regions and 419 Interim Biogeographic Regionalisation for Australia (IBRA) sub-regions. These variables included information about the interactions between the human/social system and the natural/environmental system.

The regional variables were collected into tables which showed clusters of similar regions. These regional clusters provide generalised and practical information, although caution is needed when drawing comparisons between 2 individual sites when considering potential management strategies.

Land managers can look at similar regions for insights about land-management practices that may be adopted or adapted from other regions and how interventions can be scaled up through collaboration with other regional management groups. For example, matching 2 places that have similar environments (e.g. mangroves) and similar people and communities (e.g. land tenure).

The NRM and IBRA data were used to generate a series of maps which visualise these regional comparisons and provide managers with an easy way to compare their region with similar regions.

This project also developed a decision tree to support managers when matching their region to other similar regions. The decision tree complements the region maps. Managers are guided through the process of comparing regional similarities and potential planning approaches and intervention strategies, based on observable and summarised characteristics.

To test the efficacy of this decision tree, the final report explores the extent to which planning approaches recommended when following the decision tree align with planning approaches currently in place for different regions and their relationship to social and economic factors.