Informing changes to riparian forestry rules with a Bayesian hierarchical model
Abstract In 2012 the Oregon Board of Forestry (Board) determined that current forestry rules were insufficient at preventing the degradation of cold water in salmonid bearing streams. Consequently, the Oregon Department of Forestry required a means for evaluating and comparing the effectiveness of newly proposed harvest scenarios. We derived a field data based method for simulating riparian harvest and modeling the resulting effects on stream temperature that could be used for evaluating different harvest scenarios. We simulated prescribed harvests by using previously collected riparian stand data. To create a predictive model, we modified and joined two earlier stream temperature and shade models from Groom et al. (2011b) into a Bayesian hierarchical model. The predictive model produced parameter estimates and temperature change metrics that aligned with the previous findings. The model predicted that harvest according to a full implementation of the State forest harvest plan would on average result in a 0.19 degC increase, while the model predicted that a similarly scaled harvest to current private forest regulation specifications would lead to an average increase of 1.45 degC. Further simulations suggested that employing a no cut slope distance riparian zone of 27.4 m would result in average warming below 0.3 degC of unharvested conditions. The Board considered these results along with other information and directed the Oregon Department of Forestry to develop harvest rule revisions. Those revisions became effective as of July 2017.