Informing Oregon Forestry Rule Change Decisions with a Bayesian Hierarchical Model

• Activity Number: 298 - Ecology and Environmental Policy
• Type: Contributed
• Date/Time: Tuesday, August 1, 2017 : 8:30 AM to 10:20 AM
• Sponsor: Section on Statistics and the Environment
• Author(s): Jeremiah Groom* and Lisa Madsen
• Companies: Department of Statistics and Department of Statistics
• Keywords: Bayesian ; Hierarchical ; Prediction ; Forestry ; Regulation ; Monitoring

Abstract

In 2012 current Oregon forestry rules were deemed insufficient for protecting streams against temperature increase. The Oregon Department of Forestry needed an approach for determining whether newly-proposed rule changes would protect streams from warming. Our method simulated harvest on previously-collected field data and predicted the resulting effect on stream temperature. We created a predictive Bayesian hierarchical model that linked two existing models for stream temperature and shade. We found that the Bayesian model's predictions for observed harvest values aligned with recorded temperature changes. The model predicted that harvesting all 33 sites according to the lightest harvest prescription would result in a 0.19 °C mean increase, while current harvest practices strictly applied to all stands was expected to produce an average increase of 1.45 °C. Further simulations suggested that harvesting no trees within 27 m of a stream would, on average, meet water temperature rules. The Oregon Department of Forestry's governing board considered these results along with other information and directed the Department to revise its rules. The revisions take effect in 2017.