Climate change has increased the susceptibility of forest ecosystems, resulting in escalated forest decline globally. As one of the largest forest biomasses in the Northern Hemisphere, the Eurasian boreal forests are subjected to frequent drought, windthrow, and high-temperature disturbances. Over the last century, bark beetle outbreaks have emerged as a major biotic threat to these forests, resulting in extensive tree mortality. Despite implementing various management strategies to mitigate the bark beetle populations and reduce tree mortality, none have been effective. Moreover, altered disturbance regimes due to changing climate have facilitated the success of bark beetle attacks with shorter and multivoltine life cycles, consequently inciting more frequent bark beetle-caused tree mortality. This review explores bark beetle population dynamics in the context of climate change, forest stand dynamics, and various forest management strategies. Additionally, it examines recent advancements like remote sensing and canine detection of infested trees and focuses on cutting-edge molecular approaches including RNAi-nanoparticle complexes, RNAi-symbiotic microbes, sterile insect technique, and CRISPR/Cas9-based methods. These diverse novel strategies have the potential to effectively address the challenges associated with managing bark beetles and improving forest health in response to the changing climate.

Research Highlights: Bark beetles are important agents of disturbance regimes in temperate forests, and specifically in a connected wind-bark beetle disturbance system. Large-scale windthrows trigger population growth of the European spruce bark beetle (Ips typographus L.) from endemic to epidemic levels, thereby allowing the killing of Norway spruce trees over several consecutive years. Background and Objectives: There is a lack of evidence to differentiate how outbreaks are promoted by the effects of environmental variables versus beetle preferences of trees from endemic to outbreak. However, little is known about how individual downed-tree characteristics and local conditions such as tree orientation and solar radiation affect beetle colonization of downed trees. Materials and Methods: To answer this question, we investigated the infestation rates and determined tree death categories (uprooted, broken, and stump) in wind-damaged areas in Western Tatra Mts. in Carpathians (Slovakia) from 2014–2016, following a windthrow in May 2014. In total, we investigated 225 trees over eight transects. For every tree, we measured its morphological (tree height, crown characteristics), environmental (solar radiation, terrain conditions, trunk zenith), temporal (time since wind damage), and beetle infestation (presence, location of attack, bark desiccation) parameters. We applied Generalized Additive Mixed Models (GAMM) to unravel the main drivers of I. typographus infestations. Results: Over the first year, beetles preferred to attack broken trees and sun-exposed trunk sides over uprooted trees; the infestation on shaded sides started in the second year along with the infestation of uprooted trees with lower desiccation rates. We found that time since wind damage, stem length, and incident solar radiation increased the probability of beetle infestation, although both solar radiation and trunk zenith exhibited nonlinear variability. Our novel variable trunk zenith appeared to be an important predictor of bark beetle infestation probability. We conclude that trunk zenith as a simple measure defining the position of downed trees over the terrain can anticipate beetle infestation. Conclusions: Our findings contribute to understanding of the bark beetle’s preferences to colonize windthrown trees in the initial years after the primary wind damage. Further, our findings can help to identify trees that are most susceptible to beetle infestation and to prioritize management actions to control beetle population while maintaining biodiversity.

The European spruce bark beetle Ips typographus L. causes significant economic losses in managed coniferous forests in Central and Northern Europe. New infestations either occur in previously undisturbed forest stands (i.e., spot initiation) or depend on proximity to previous years’ infestations (i.e., spot spreading). Early identification of newly infested trees over the forested landscape limits the effective control measures. Accurate forecasting of the spread of bark beetle infestation is crucial to plan efficient sanitation felling of infested trees and prevent further propagation of beetle-induced tree mortality. We created a predictive model of subsequent year spot initiation and spot spreading within the TANABBO decision support system. The algorithm combines open-access Landsat-based vegetation change time-series data, a digital terrain model, and forest stand characteristics. We validated predicted susceptibility to bark beetle attack (separately for spot initiation and spot spreading) against beetle infestations in managed forests in the Bohemian Forest in the Czech Republic (Central Europe) in yearly time steps from 2007 to 2010. The predictive models of susceptibility to bark beetle attack had a high degree of reliability (area under the ROC curve - AUC: 0.75-0.82). We conclude that spot initiation and spot spreading prediction modules included within the TANABBO model have the potential to help forest managers to plan sanitation felling in managed forests under pressure of bark beetle outbreak.

Machine learning algorithms (MLAs) are used to solve complex non-linear and high-dimensional problems. The objective of this study was to identify the MLA that generates an accurate spatial distribution model of bark beetle (Ips typographus L.) infestation spots. We first evaluated the performance of 2 linear (logistic regression, linear discriminant analysis), 4 non-linear (quadratic discriminant analysis, k-nearest neighbors classifier, Gaussian naive Bayes, support vector classification), and 4 decision trees-based MLAs (decision tree classifier, random forest classifier, extra trees classifier, gradient boosting classifier) for the study area (the Horní Planá region, Czech Republic) for the period 2003–2012. Each MLA was trained and tested on all subsets of the 8 explanatory variables (distance to forest damage spots from previous year, distance to spruce forest edge, potential global solar radiation, normalized difference vegetation index, spruce forest age, percentage of spruce, volume of spruce wood per hectare, stocking). The mean phi coefficient of the model generated by extra trees classifier (ETC) MLA with five explanatory variables for the period was significantly greater than that of most forest damage models generated by the other MLAs. The mean true positive rate of the best ETC-based model was 80.4%, and the mean true negative rate was 80.0%. The spatio-temporal simulations of bark beetle-infested forests based on MLAs and GIS tools will facilitate the development and testing of novel forest management strategies for preventing forest damage in general and bark beetle outbreaks in particular.