Slow and fast drivers of the natural disturbance regime in Central European forest ecosystems

Thom, Dominik; Seidl, Rupert; Steyrer, Gottfried; Krehan, H.; Formayer, Herbert

doi:10.1016/j.foreco.2013.07.017

Cited by 106 publications

(93 citation statements)

References 44 publications

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“…Although we have only used a minimal set of expert-based approximations for model parameterization, the results for bark beetle predisposition compare well with recent empirical studies (e.g., [8,38,39]) which found that high shares of Norway spruce and drought increased the susceptibility for bark beetle infestations. This is further amplified for situations where two or even more insect generations can develop within one growing season [40,41].…”

Section: Model Justificationsupporting

confidence: 73%

“…Netherer and Nopp-Mayr [37] confirmed the important relationship between bark beetle disturbances and stand canopy closure. Thom et al [38] described that older stands with a higher proportion of spruce leads to increased disturbances. However, there is reason to believe that the predisposing effect on bark beetle infestations increase on stand ages >100 years [39].…”

Section: Model Justificationmentioning

confidence: 99%

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

Radl¹,

Lexer²,

Vacik³

2017

Forests

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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.

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Section: Model Justificationsupporting

confidence: 73%

Section: Model Justificationmentioning

confidence: 99%

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

Radl¹,

Lexer²,

Vacik³

2017

Forests

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“…For example, secondary damage resulting from bark beetles outbreaks in the follow-up to large disturbances (Wermelinger et al 2002) are responsible, on average, for extra damage of between 10 and 25 % of initial wind damage (Stadelmann et al 2013). Thom et al (2013) demonstrated that for every cubic meter of bark beetle damage in the current year, 0.56 m 3 of additional bark beetle damage is expected in the following year. This not only means that sanitary concerns must be integrated as soon as possible in the decision-making scheme , but it emphasizes the need for an advanced cost-benefit analysis to inform decisions.…”

Section: Assessing Systemic Impacts Of Stormsmentioning

confidence: 99%

Integrated and systemic management of storm damage by the forest-based sector and public authorities

Riguelle

Hebert

Jourez

2016

Annals of Forest Science

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“…In recent years our quantitative understanding of disturbance processes has been increased regarding study improvements (SEIDL et al, 2011;THOM et al, 2013). These researchers prepared information on the historical range of variability of forest ecosystems to manage wind disturbance (KEANE et al, 2009).…”

Section: Introductionmentioning

confidence: 99%

Prediction Spatial Patterns of Windthrow Phenomenon in Deciduous Temperate Forests Using Logistic Regression and Random Forest

Shabani

Moslem

2017

CERNE

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Forest management needs to evaluate various hazards where may cause economic or other losses to forest owners. The aim of this study is to prepare windthrow hazard maps based on logistic regression and random forest models in Nowshahr Forests, Mazandaran Province, Iran. First of all, 200 windthrow locations were identified from extensive field surveys and some reports. Out these, 140 (70%) locations were randomly selected as training data and the remaining 60 (30%) cases were used for the validation goals. In the next step, 10 predictive variables such as slope degree, slope aspect, altitude, Topographic Position Index (TPI), Topographic Wetness Index (TWI), distance to roads and skid trails, wind effect, soil texture, forest type and stand density were extracted from the spatial database. Subsequently, windthrow hazard maps were produced using logistic regression and RF models, and the results were plotted in ArcGIS. Finally, the area under the curves (AUC) and kappa coefficient were made for performance purposes. The validation of results presented that the area under the curve and kappa have a more accuracy for the random forest (97.5%, and 95%, respectively) than logistic regression (96.667%, and 93.333%, respectively) model. Therefore, this technique has more potentiality to be applied in the evaluation of windthrow phenomenon in forest ecosystems. Additionally, both models indicate that the spatial distribution of windthrow incidence likelihood is highly variable in this region. In general, the mentioned findings can be applied for management of future windthrow in favor of economic benefits and environmental preservation.

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Slow and fast drivers of the natural disturbance regime in Central European forest ecosystems

Cited by 106 publications

References 44 publications

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

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

Integrated and systemic management of storm damage by the forest-based sector and public authorities

Prediction Spatial Patterns of Windthrow Phenomenon in Deciduous Temperate Forests Using Logistic Regression and Random Forest

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