Exploring patterns in risk factors for bark beetle attack during outbreaks triggered by drought stress with harvester data on attacked trees: A case study in Southeastern Sweden

Sammanfattning: ABSTRACT Raising temperatures and climate variability have intensified extreme weather events worldwide. These extremes can enhance and trigger possible pest outbreaks. Bark beetle attacks have become a major concern in regions with extensive spruce forest areas. Southeastern Sweden has faced repeated outbreaks resulting in widespread tree loss. By observing historic climate data for the main counties of Southeastern Sweden we can notice that the temperature is progressively raising. Rainfall do not manifest a clear pattern but it follows a descending trend. Water shortage can lead to drought events for the specific area. The main triggering factors for a bark beetle outbreak are a storm damage or a drought stress. In this study we used data and try to model an outbreak that was triggered by drought stress in 2018. This study focused on proactive risk management. Data from 2018 and 2019,2020 were combined to create two main datasets. One from 2018 which was considered a drought year and one that emerge from the years 2019 and 2020 which were considered more “normal” year from a meteorological perspective. The model used overlay analysis in ArcGIS environment between attacked trees and predisposing factors like soil moisture, topography and landcover data. Th goal was to investigate intervals of risk factors that could raise the potential risk. These factors could in some cases attribute higher danger in specific regions. Furthermore, a risk map was created by utilizing a weighted overlay model. All the abovementioned parameters were considered of equal weight. Some final things to consider about improving the specific model. Firstly, the unique type of the dataset that contained attacked trees. This dataset was collected by harvester machines which were equipped with GNSS devices. The attacked tree was attributed the coordinates of the body of harvester or the crane. This could result in a different landcover classification than the actual and it is an uncertainty in the findings of this study. A soil wetness index was used to examine the effect of soil wetness. This was a static dataset. It was the same dataset for the 2018 and 2020. Possible correlations between risk factors were not examined. Topography and soil moisture are considered to correlate in some cases but it was hard to quantify in this study. No statistical test was used to support the findings. Finally, the spatial propagation of the outbreak was not imported as a parameter in the specific model. It is possible that the outbreak started in 2018, cumulatively raised the numbers of bark beetle in the following years.