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.

Habitat suitability analysis is complex and requires integration of multiple attributes at a range of spatial scales. We use fine- and broad-scale variables within a spatial modelling framework to determine the suitability of greater sage-grouse nesting habitat using fuzzy logic. Fuzzy theory allows for a landscape component to have full, partial, or no membership in a particular management target. We used seven biophysical and anthropogenic variables correlated with nest site locations to build the fuzzy model. Using field and remotely sensed data and expert opinion, we developed fuzzy functions to define numerical membership values describing relationships between landscape characteristics and nesting habitat suitability. We used a fuzzy gamma overlay function to define five suitability classes in the resulting habitat map. Using a fuzzy logic model, we were able to spatially distinguish the suitability of nesting habitat using fine- and broad-scale attributes and characterise the uncertainty of the habitat model.

Native bark beetles (Coleoptera: Curculionidae: Scolytinae) are a multi-species complex that rank among the key disturbances of coniferous forests of western North America. Many landscape-level variables are known to influence beetle outbreaks, such as suitable climatic conditions, spatial arrangement of incipient populations, topography, abundance of mature host trees, and disturbance history that include former outbreaks and fire. We assembled the first open access data, which can be used in open source GIS platforms, for understanding the ecology of the bark beetle organism in Alaska. We used boosted classification and regression tree as a machine learning data mining algorithm to model-predict the relationship between 14 environmental variables, as model predictors, and 838 occurrence records of 68 bark beetle species compared to pseudo-absence locations across the state of Alaska. The model predictors include topography- and climate-related predictors as well as feature proximities and anthropogenic factors. We were able to model, predict, and map the multi-species bark beetle occurrences across the state of Alaska on a 1-km spatial resolution in addition to providing a good quality environmental dataset freely accessible for the public. About 16% of the mixed forest and 59% of evergreen forest are expected to be occupied by the bark beetles based on current climatic conditions and biophysical attributes of the landscape. The open access dataset that we prepared, and the machine learning modeling approach that we used, can provide a foundation for future research not only on scolytines but for other multi-species questions of concern, such as forest defoliators, and small and big game wildlife species worldwide.