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Forests 2018, 9(1), 15; doi:10.3390/f9010015

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

Institute of Silviculture, University of Natural Resources and Life Sciences (BOKU), Vienna 1190, Austria
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Received: 29 October 2017 / Revised: 11 December 2017 / Accepted: 20 December 2017 / Published: 26 December 2017
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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. View Full-Text
Keywords: Bayesian networks; forest management; uncertainty; expert elicitation Bayesian networks; forest management; uncertainty; expert elicitation
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Radl, A.; Lexer, M.J.; Vacik, H. A Bayesian Belief Network Approach to Predict Damages Caused by Disturbance Agents. Forests 2018, 9, 15.

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