4.7 Article

Combining an Occurrence Model and a Quantitative Model for the Prediction of the Sanitary Felling of Norway Spruce Because of Bark Beetles

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FORESTS
卷 13, 期 2, 页码 -

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MDPI
DOI: 10.3390/f13020319

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sanitary felling; prediction; Ips typographus; Picea abies; Slovenia; forecasting; insect outbreak forest pest

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This study developed a predictive model for the sanitary felling of Norway spruce due to bark beetles. The model identified various factors that are correlated with sanitary felling and can be used to predict the future occurrence. A combination of occurrence model and quantitative model was used to perform a prediction for the next year.
The European spruce bark beetle (Ips typographus L.) is an eruptive forest pest that has caused a great deal of damage in the last decades because of increasing climatic extremes. In order to effectively manage outbreaks of this pest, it is important to predict where they will occur in the future. In this study we developed a predictive model of the sanitary felling of Norway spruce (Picea abies (L.) H. Karst.) because of bark beetles. We used a time series of sanitary felling because of bark beetles from 1996 to 2020 in Slovenia. For the explanatory variables, we used soil, site, climate, geographic, and tree damage data from the previous year. The model showed that sanitary felling is negatively correlated with slope, soil depth, soil cation exchange capacity, and Standard Precipitation Index (less sanitary felling in wet years). On the other hand, soil base saturation percentage, temperature, sanitary felling because of bark beetles from the previous year, sanitary felling because of other abiotic factors from the previous year, and the amount of spruce were positively correlated with the sanitary felling of Norway spruce due to bark beetles. The model had an R-2 of 0.38. A prediction was performed for 2021 combining an occurrence model and a quantitative model. The model can be used to predict the amount of sanitary felling of Norway spruce due to bark beetles and to refine the risk map for the next year, which can be used for forest management planning and economic loss predictions.

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