Causal Networks to Inform Decisions for Ecological Restoration

Area of Science:

• Environmental Science
• Ecology
• Restoration Ecology

Background:

• Anthropogenic activities like mining and oil extraction release environmental contaminants.
• Ecosystem recovery requires remediation (reducing contaminants) and restoration (improving habitats and wildlife).
• Assessing restoration effectiveness is crucial for ecological health and regulatory compliance.

Purpose of the Study:

• To demonstrate the application of Bayesian Decision Networks (BDNs) in Natural Resource Damage Assessment and Restoration (NRDAR) cases.
• To evaluate the effectiveness of different restoration actions on wildlife populations and ecosystem recovery.
• To provide a decision advisory tool for restoration practitioners.

Main Methods:

• Utilized Bayesian Decision Networks (BDNs) for two case studies: a stylized riparian restoration and a real-world case in the Upper Arkansas River (UAR).
• Modeled restoration actions for Song Sparrow populations following mine remediation.
• Applied BDNs to assess and forecast Brown Trout population recovery in response to habitat restoration in the UAR.

Main Results:

• The riparian restoration model indicated that increased restoration costs could reduce the time to reach baseline Song Sparrow populations.
• The UAR model demonstrated Brown Trout population recovery (uplift) due to improved instream habitat restoration, with forecasted improvements.
• BDN models showed trade-offs between restoration costs and expected ecological recovery.

Conclusions:

• Bayesian Decision Networks are adaptable and applicable to diverse restoration sites, resources, and actions.
• Causal network modeling can serve as a decision support tool for environmental restoration practitioners.
• BDNs offer a structured approach to evaluating and forecasting restoration effectiveness in NRDAR.