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Environmental Offsets: Maximizing Ecosystem Services from Biodiversity Conservation

Open Cut » Environment

Published: June 13Project Number: C19033

Get ReportAuthor: Magnus Wettle, Damian Barrett, Anthony Almarza, Birte Schoettker | The University of Queensland

Access to land for mining is becoming increasingly difficult for mine establishment and expansion, particularly in areas where the land is fully allocated to food and fibre production, urban settlement, or environmental protection. A key tenet of sustainable development is that production does not compromise the basic ecosystem functioning that provides environmental goods and services (collectively called 'ecosystem services') despite disturbance to the landscape. When competition for land becomes intense, or where ecosystem services are potentially compromised, environmental offsets provide one means of breaking the deadlock.

 

The objective of project C19033 was to improve the business case for environmental offsets by providing sound scientific analyses as the basis for their utilization. The project has established new methods for assessing regional environmental benefits of actions in terms of both conservation of species and ecosystems, and delivery of ecosystem services. These methods bring together biophysical assessment of ecosystem function and a spatial optimization framework to quantify costs and benefits of

particular conservation actions.

 

Highlights include:

· Successful development of a novel, model-data assimilation and inversion scheme for deriving plant functional trait parameters, and by extension, ecosystem services such as surface runoff;

· The merger of state-of-the art remote sensing data products for assessing and benchmarking the condition of vegetation across the Bowen Basin study area;

· Successful implementation of purpose-built genetic algorithm spatial optimization software for optimal identification of re-vegetation sites, and

· A case study illustrating benefits and trade-offs for vegetation initiatives, as well as potential optimal locations, for the study area.

 

In this region, the study showed that attempting to achieve all management goals requires trade-offs (increasing costs of compromise) among each of the goals. As more goals are included, a greater trade-off with any single goal is required.

 

Attempting to simultaneously achieve environmental goals of reduced runoff (sedimentation) and increased carbon storage by vegetation conflicts with socioeconomic goals. A similar result occurs when environmental and biodiversity goals are targeted, although to a lesser extent, and with the biodiversity goal being relatively more compromised. By contrast, achievement of biodiversity and socioeconomic goals could be realised with little trade-off between these goals, indicating that they are mutually inclusive. However, when all three management goals are to be achieved simultaneously, all three goals experience compromise, with the two most impacted goals being between ecosystem and socioeconomic goals.

 

The software generates maps of the spatial distribution of revegetation sites and it is shown that the dynamic compacting term of the objective function effectively clusters re-vegetation sites, thereby avoiding sprinkling re-vegetation throughout the study region. When the optimisation is weighted towards avoiding towns, revegetation sites are chosen in the western part of the study region, away from the main towns of Emerald, Blackwater and Dysart while still achieving biodiversity, socioeconomic and environmental goals of re-vegetation programs.

 

Results from application of the genetic algorithm approach provide a range of equally good potential solutions where any individual solution is fit-for-purpose and provides an option for further investigations to determine final suitability as a revegetation site. Importantly, these solutions are strongly dependent on the weightings, or priorities, applied to each of the management goals.

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