Restoration Prioritisation


Conservation has many facets, ranging from biodiversity protection to management of invasive species to restoration of degraded lands. Globally, forest biomes have been subjected to extensive land clearing generating significant carbon emissions, depleting important ecosystem services and threatening biodiversity. In many cases, protection alone may not be sufficient and restoration is urgently needed to reverse human damage and prevent ongoing attrition of species from isolated forest fragments and reinstate ecosystem services. Increasing ambitious initiatives have been proposed to respond to these challenges e.g., Bonn Challenge Ministerial Roundtable target to restore 150 million hectares of lost forests and degraded land worldwide by 2020 (IUCN). Nevertheless, ecological restoration is a complex endeavor and prioritization of where, when and how habitat is restored is likely to be critical in managing risks and ensuring that desired outcomes are delivered in a way that represents good value for money and supported by key stakeholders.


Kerrie Wilson, Luke Shoo, Valerie Hagger, Brooke Williams, Konrad Uebel


Erik Meijaard, Sugeng Budiharta, Carla Cattarall


Irvine Ranch Natural Landmark

The Irvine Ranch Natural Landmark is a collection of permanently protected wildlands and parks located near the Santa Ana Mountains in Southern California. It represents approximately 44,000 acres of land, much of which has been degraded by anthropological disturbances and fire. Managers of the Irvine Ranch sought to prioritise funding over the next 20 years to achieve the best possible results for restoration.

To date there has been little work on the systematic prioritisation of restoration activities, so this project represented a novel application of spatial conservation planning theory. We worked with Dr Jutta Burger, Dr Megan Lulow and Yi-Chin Fang at the Irvine Ranch Conservancy to formulate the restoration prioritisation problem and developed a return on investment based approach for determining robust restoration priorities for a 20 year planning horizon. Problem complexities included a need to account for multiple objectives, time lags, logistical constraints, stochastic occurrences of fire and drought, connectivity and uncertain outcomes.

Outcomes from the project included:

  • A problem definition paper for prioritizing restoration activities (McBride et al, 2010)
  • An application paper, detailing how we specifically applied our methodology to the Irvine Ranch Landmark case study (Wilson et al, 2011)
  • Development of a restoration prioritization decision support tool for use by Ranch managers to implement and update restoration priorities over the next 20 years.
Priority areas for restoration after 20 years. Irvine Range Project

Priority areas for restoration after 20 years. Irvine Range Project


Using East Kalimantan as a case study, we are prioritising degraded forest for restoration and determining which restoration actions should be implemented across multiple ecosystem types. We identified 400,000 hectares of highly degraded lowland forest in East Kalimantan, for which restoration was cost-effective (Budiharta et al., 2014). This research revealed degraded areas that should not be converted to other land uses, such as palm oil. Instead these areas could be the focus of privately funded ecosystem restoration concessions (ERCs) and contribute to the government target of creating 2.5 million hectares of ERC (currently only 397,000 hectares of ERC licenses have been granted).

City of Gold Coast

Over a quarter of Australia’s native forest and woodlands have been cleared since European settlement, and vegetation restoration is urgently needed to avoid further loss of species and ecosystem services (such as clean air and water). Through a collaborative project with City of Gold Coast we are developing new theory and methods to help environmental managers allocate restoration funds for vegetation recovery in a way that addresses the tensions between risk aversion and aspirations to maximise return on investment. In this project our restoration ecologists and decision scientists are partnering with natural area managers from the City of Gold Coast, to make public expenditure on restoration more effective, efficient and transparent…read more


Read these stories in Decision Point magazine…

DPoint86_coverAllocating funds among restoration actions

A major emerging task for biodiversity conservation is to ‘scale-up’ the restoration of degraded land from the local patch to the scale of the landscape (regional). This poses significant challenges for prioritising investments, most notably because: (a) restoring native vegetation involves considerable uncertainty and time lags over at least several decades; and (b) restoration typically involves a range of different potential actions, each with its own costs, time frame and likelihood of success.

In this workshop we aimed to directly address the tension between minimizing shortfall risk (not achieving desired targets) and maximizing return on investment… read more

DPoint86_coverPrioritising restoration in Kalimantan

Mention Indonesia and images of soaring rainforests and orangutans come to mind. But the reality is quite different. Over 63% of Indonesia’s forest estate is currently deforested or degraded (that’s around 83 million hectares), and many of its iconic species such as the orangutan and proboscis monkeys are endangered. And the deforestation marches on. In 2012 Indonesia broke the record for clearing tropical forest. The choking haze from burning forest and peatland has blanketed South East Asia many times in recent years, and awareness of the economic and health hazards associated with this is growing.

Ambitious goals… read more

Key references

Guerrero AM, Shoo L, Iacona G, Standish RJ, Catterall CP, Rumpff L, de Bie K, White Z, Matzek V, Wilson K.A. 2017. Using structured decision making to set restoration objectives when multiple values and preferences exist. Restoration Ecology. 25(6): 853-865.

Hagger, V., Dwyer, J. and Wilson, K. 2017. What motivates ecological restoration? Restoration Ecology. 25(5): 832-843.

Shoo, L.P., Catterall, C.P., Nicol, S., Christian, R., Rhodes, J., Atkinson, P., Butler, D., Zhu, R., Wilson, K.A. 2017. Navigating complex decisions in restoration investment. Conservation Letters. Conservation Letters. 10(6):748-756

Budiharta, S, Meijaard E, Wells J.A, Abram N.K, and Wilson, K.A. 2016. Enhancing feasibility: incorporating a socio-ecological systems framework into restoration planning. Environmental Science & Policy. 64: 83-92.

Evans, M. C., Carwardine, J., Fensham, R.J., Butler, D., Wilson, K.A., Possingham, H.P., and Martin, T.G. 2015. Carbon farming via assisted natural regeneration as a cost-effective mechanism for restoring biodiversity in agricultural landscapes. Environmental Science & Policy. 50: 114-129.

Budiharta S., Meijaard E., Erskine P.D., Rondinini C., Pacifici M. & Wilson K.A. 2014. Restoring degraded tropical forests for carbon and biodiversity. Environmental Research Letters. 9, 114020.

Shoo, L. P., Scarth, P., Schmidt, S. and Wilson, K. A. (2013), Reclaiming Degraded Rainforest: A Spatial Evaluation of Gains and Losses in Subtropical Eastern Australia to Inform Future Investment in Restoration. Restoration Ecology. 21: 481–489. doi: 10.1111/j.1526-100X.2012.00916.x

Wilson, K. A., Lulow, M., Burger, J. and McBride, M. F. (2012). “The economics of restoration.” In L. David, M. Palle and S. John (eds.), Forest landscape restoration: integrating natural and social sciences pp. 215-231). New York, NY, United States: Springer. doi: 10.1007/978-94-007-5326-6_11

Wilson, K.A., Lulow, M., Burger, J., Fang Y-C, Andersen, C., Olson D., O’Connell, M., and McBride M.F. (2011). Optimal restoration: accounting for space, time, and uncertainty. Journal of Applied Ecology. 48:715-725. doi: 10.1111/j.1365-2664.2011.01975.x

McBride, M.F., Wilson, K.A., Burger, J., Fang, Y.-C., Lulow, M., Olson, D., O’Connell, M. and Possingham, H.P. (2010), Mathematical problem definition for ecological restoration planning. Ecological Modelling. 221 (19): 2243-2250 doi:10.1016/j.ecolmodel.2010.04.012