Fine-resolution conservation planning with limited climate-change information

Area of Science:

• Conservation science
• Climate change adaptation
• Ecological economics

Background:

• Climate change introduces significant uncertainty into conservation planning, complicating traditional approaches to allocating resources.
• A finance-derived risk-diversification strategy can be applied to conservation investments to mitigate uncertainty, but it is data-intensive and requires numerous ecological forecasts.
• Existing methods struggle with fine-resolution conservation planning due to the need for extensive climate change scenario data.

Purpose of the Study:

• To develop and test an iterative, spatial portfolio analysis technique for conservation resource allocation under limited climate change information.
• To enable effective conservation planning in data-scarce environments by optimizing the use of available ecological forecasts.
• To assess the impact of limited climate information on conservation returns and the efficacy of the proposed iterative approach.

Main Methods:

• Developed an iterative, spatial portfolio analysis technique adaptable to varying levels of subregional planning.
• Applied the technique to the Prairie Pothole Region, a key North American conservation landscape.
• Compared conservation outcomes under limited versus full climate change information scenarios.

Main Results:

• Limited climate change information resulted in a potential 30% difference in expected conservation returns in the Prairie Pothole Region.
• The developed iterative approach enabled finer resolution portfolio allocation despite insufficient climate forecasts.
• The most efficient iterative approach reduced the expected loss in conservation outcomes by 17% compared to other iterative methods.

Conclusions:

• The iterative spatial portfolio analysis is a viable method for optimizing conservation investments amidst climate-driven uncertainty and data limitations.
• Even with limited data, the approach achieves the best possible risk-return combinations for conservation resource allocation.
• This technique offers a practical solution for improving conservation planning effectiveness in data-limited regions facing climate change.