A Bayesian Belief Network Approach to Predict Damages Caused by Disturbance Agents

Abstract

Abstract:In mountain forests of Central Europe, storm and snow breakage as well as bark beetles are the prevailing major disturbances. The complex interrelatedness between climate, disturbance agents, and forest management increases the need for an integrative approach explicitly addressing the multiple interactions between environmental changes, forest management, and disturbance agents to support forest resource managers in adaptive management. Empirical data with a comprehensive coverage for modelling the susceptibility of forests and the impact of disturbance agents are rare, thus making probabilistic models, based on expert knowledge, one of the few modelling approaches that are able to handle uncertainties due to the available information. Bayesian belief networks (BBNs) are a kind of probabilistic graphical model that has become very popular to practitioners and scientists mainly due to considerations of risk and uncertainties. In this contribution, we present a development methodology to define and parameterize BBNs based on expert elicitation and approximation. We modelled storm and bark beetle disturbances agents, analyzed effects of the development methodology on model structure, and evaluated behavior with stand data from Norway spruce (Picea abies (L.) Karst.) forests in southern Austria. The high vulnerability of the case study area according to different disturbance agents makes it particularly suitable for testing the BBN model.

Dynamic Bayesian Networks to Assess Anthropogenic and Climatic Drivers of Saltwater Intrusion: A Decision Support Tool Toward Improved Management

“…In terms of model sensitivity, both the sensitivity to findings, which measures how the posterior probability distributions (PPD) change under different conditions, and the sensitivity to parameters, which measures how PPD change when input variables are modified, were implemented (Pollino et al 2007; Chen and Pollino 2012; Moe et al 2016; Radl et al 2018). Sensitivity to findings was quantified through the calculation of the value of information (VOI) (Pollino et al 2007; Marcot 2012; Radl et al 2018). The VOI identifies the variables with the highest mutual information with respect to a selected target node.…”

Decision Support Approaches in Adaptive Forest Management

“…Forest management regimes were optimized while potential fire effects were simulated in this decision-support system that facilitated an examination of budgetary constraints. Radl et al [8] describe the development of a Bayesian model designed to inform management planning processes that recognize natural disturbances (wind events and insect outbreaks). The model was applied in a case study to forests of southern Austria in an effort to analyze the trade-offs among management options of Norway spruce (Picea abies).…”