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Causal Networks to Inform Decisions for Ecological Restoration.

Christopher J Kotalik1, Freya E Rowland2, Bruce G Marcot3

  • 1U.S. Geological Survey, Columbia Environmental Research Center, Columbia, MO, USA. ckotalik@usgs.gov.

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Summary
This summary is machine-generated.

Bayesian Decision Networks (BDNs) aid environmental restoration by modeling contaminant impacts and restoration actions. These networks help predict recovery times and costs for ecosystems, offering a valuable decision tool for practitioners.

Keywords:
Causal networkDecision advisory toolHabitat restorationNatural Resource Damage Assessment and Restoration

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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.