The development of an integrated systems model for balancing coral reef health, land management and tourism risks on the Great Barrier Reef

Thomas, C.R., Gordon, I.J., Wooldridge, S., Van Grieken, M., and Marshall, P. (2009) The development of an integrated systems model for balancing coral reef health, land management and tourism risks on the Great Barrier Reef. In: Proceedings of MODSIM09 - International Conference on Modelling and Simulation. pp. 4346-4352. From: Proceedings of MODSIM09 - International Conference on Modelling and Simulation, 13-17 July 2009, Cairns, QLD, Australia.

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A prototype Bayesian belief network (BBN) is described that provides catchment-to-reef integration of previously unlinked components of the Great Barrier Reef (GBR) social-ecological system.

The BBN is developed to help decision-makers understand the socio-economic trade-offs associated with managing for resilient reef communities given the threat posed by climate change. The probability of severe coral bleaching events increases with climate-driven increases in surface ocean temperatures, but this threat is synergistically linked to the water quality within the GBR lagoon. Improved inshore water quality requires the adoption of 'best practice' catchment management, which may incur considerable cost to the agricultural industry. However, this cost is countered by the associated benefit of safeguarding future reef tourism. The aim of this work is to develop a prototype model capable of investigating these key system linkages only. The BBN formalises this socio-ecological cost-benefit analysis within a risk assessment framework. This aids the difficult task of prioritising alternative management actions. The complexity of the problem represents a challenging modelling task with a large envelope of solutions needing to be represented, each with its own scale and configuration of 'wins' and 'losses' across diverse system sectors. To simplify the modelling task, we specifically targeted key elements of the reef, agriculture, and tourism sectors and then focused on developing the most parsimonious set of cross-sector linkages to generate an integrated systems model. We focus here on the approach used, as results are not yet available. The diverse nature of the individual sectors presented a major challenge for model construction, not least because the causal (i.e. dependence) relationships within individual sectors exist at different levels of understanding and scientific development, as do the relationships between the separate sectors. Furthermore, the data that captures the functional behaviour of each sector (as well as cross-sector interactions) exists as an eclectic mix of simulated, empirical and subjectively-derived information. Fortunately, the adopted BBN approach is capable of resolving these system domain and data uncertainties in a transparent fashion, which includes the assigning of error estimates for the alternate system trade-off scenarios. By making these trade-off uncertainties explicit, the resultant framework provides decision-makers with a rational (i.e. quantitative) method to resolve catchment level questions such as;

Which reef protection target provides the lowest risk and maximum benefit for the local community?

How soon must reef protection targets be realised in order to maximise cross-sector benefits?

Can win/win strategies be pursued with acceptable levels of certainty?

For a given reef protection target, what are the costs to industry and how are they distributed across sectors?

What are the risks and benefits of maximum and 'do nothing' reef protection targets, and how are these risks and benefits distributed?

Are the economic benefits to tourism likely to be large enough to balance economic losses to agriculture?

Are economic losses in any sector likely to exist at levels that substantially reduce community wellbeing?

What are the most influential system components, and are they amenable to policy development?

The framework is currently under review by participants. Once the structure is verified, the prototype will be parameterised and evaluated.

Item ID: 42655
Item Type: Conference Item (Research - E1)
ISBN: 978-0-9758400-7-8
Keywords: decision support, water quality, Bayesian belief network, ecosystem services, risk trade-offs
Funders: Marine and Tropical Sciences Research Facility, CSIRO
Date Deposited: 10 Feb 2016 07:43
FoR Codes: 05 ENVIRONMENTAL SCIENCES > 0502 Environmental Science and Management > 050205 Environmental Management @ 100%
SEO Codes: 96 ENVIRONMENT > 9605 Ecosystem Assessment and Management > 960507 Ecosystem Assessment and Management of Marine Environments @ 100%
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