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Abstract:
Livelihoods that integrate natural resource management, health, family wellbeing, cultural knowledge, biodiversity and income are necessary to sustain remote Australian Aboriginal groups. They are major landowners in the 70% of Australia that is arid. Recently, new drivers from climate to weeds have transitioned arid ecosystems into states very different from those long managed with indigenous knowledge. Low levels of literacy and income, and high levels of chronic disease and social stress exacerbate the ecological challenge. A simple, flexible framework is needed to build a shared concept of these complex socio-ecological systems among Aboriginal groups, NGOs and government over many years. This modelling language must translate between sand drawings and computer models, and value indigenous and government indicators, to effectively support partnerships between Aboriginal and government groups. It must also function adequately without modellers, to be usable in widely dispersed settlements. We are building a community of practice among researchers and remote Aboriginal communities to unite participatory planning and systems modelling with new data on the traditional relationships among environment, health and wellbeing. Our strategy focuses on: 1. a cross-sectoral participatory modelling that uses locally-designed systems diagrams to collect de-identified data (eg. outcomes, indicators, and interactions); 2. an iterative process of translating, sharing and synthesizing model drafts; 3. scenario-building to test model usage behaviours; 4. the endpoint of an online planning and evaluation tool to enable mutual accountability between regional investments and local outcomes. This paper will present early experiences in the process of gradually constructing a shared and flexible framework, a detailed example of one livelihood model, and inferences about model usage. I will also present challenges that include: identifying model features and structures that are meaningful across cultures; identifying indicators and surrogates; measures that link cause-effect in models and in community experiences; and verifying models.
