A Bayesian Approach to Landscape Ecological Risk Assessment Applied to the Upper Grande Ronde Watershed, Oregon

Abstract

We present a Bayesian network model based on the ecological risk assessment framework to evaluate potential impacts to habitats and resources resulting from wildfire, grazing, forest management activities, and insect outbreaks in a forested landscape in northeastern Oregon. The Bayesian network structure consisted of three tiers of nodes: landscape disturbances, habitats, and the ecological resources or endpoints of interest to land managers. Nodes at each tier were linked to lower nodes if ecological and spatial relationships existed between them. All parameters had four potential discrete states: zero, low, medium, and high. Our model reliably predicted probable risk to habitats and endpoints from natural and anthropogenic disturbances. The disturbances most likely to transform habitats and effect ecological resources were forest management and wildfire. Of the six habitats, moist forest (characterized by Douglas fir and grand fir) was found to be at greatest risk of ecological impacts. The management endpoint with the highest likelihood of impact was historical range of variability (HRV) for salmon habitat, followed by recreation (hunting native ungulates) and HRV wildfire. We found that the Bayesian approach to ecological risk assessment was a useful method to assess potential impacts to ecological resources resulting from forest management and natural disturbances.

New approaches to the ecological risk assessment of multiple stressors

“…The validity of such extrapolations is largely untested. The traditional risk-analysis process lacks transparency, and estimates of risk and associated uncertainty may be difficult to communicate to stakeholders (Ayre and Landis 2012). There is little knowledge about how community composition influences responses to contaminants.…”

A regional-scale ecological risk framework for environmental flow evaluations

“…The PROBFLO framework is based on 10 procedural RRM steps , and it incorporates BN development and evaluation procedures Ayre and Landis, 2012) into a robust E-flow assessment method that gives emphasis to adaptive management for holistic E-flow management (Fig. 3).…”

“…The RRM has been applied to evaluate a range of natural and anthropogenic stressors including water pollution, diseases, alien species and a range of altered environmental states (Walker et al, 2001;Moraes et al, 2002;Hayes and Landis, 2004;Colnar and Landis, 2007;Anderson and Landis, 2012;Ayre and Landis, 2012;Bartolo et al, 2012;O'Brien et al, 2012;Hines and Landis, 2014;Ayre et al, 2014). This tool can be used to carry out holistic, probabilistic assessments of the risk to the availability and conditions of ecosystem service and ecological endpoints, and facilitate socio-ecological trade-offs.…”

Big data opportunities and challenges for assessing multiple stressors across scales in aquatic ecosystems

“…expert opinion) with quantitative primary data (Bayliss et al 2012). This goes some way to reduce uncertainty, which is inherent in complex ecological systems (Ayre and Landis 2012). The incorporation of large primary datasets into ecological risk assessment to inform environmental regulation is addressed in detail by Van den Brink et al (2016).…”

“…Furthermore, it presumes substantial local knowledge among those experts for its application in specific situations, whereas stressors are defined in broad, overlapping categories, conflating potential risks and potentially leading to confounding errors. Other strategies developed in the United States and Australia seek to address the uncertainty inherent in large-scale assessments of the cumulative impacts of multiple disparate stressors, by incorporating Bayesian methods into ecological risk assessment (Wiegers et al 1998;Pollino et al 2007;Ayre and Landis 2012;Bayliss et al 2012). These models are often flexible with the potential to integrate qualitative knowledge (e.g.…”