This research is dedicated to solving an urgent problem associated with the large-scale destruction of taiga forests by Siberian silk moth (Dendrolimus sibiricus) outbreaks. The dynamics of the damage to dark coniferous forest stands induced by the Siberian silk moth outbreaks in mid-altitude mountains were studied. A hypothesis was formulated based on the fundamental influence of the orography on the phytophage’s dispersal within the landscape, along with the climate, which acts as a secondary predictor—a catalyst for outbreaks. The study was carried out using Landsat−8 satellite imagery time-series (from 2018 to 2020). The data were verified using a field forest pathological survey of the territory. An assessment of the defoliated forest area and damage association with the landscape was carried out using an Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) digital elevation model. The assessment was aimed to detail the forecast parameters for an outbreak development in mid-altitude mountains using the orographic features—altitude, terrain slope, and slope aspect. Early warnings of phytophagous insect outbreaks in mountain southern taiga should be focused on the permanent monitoring of dark coniferous stands of the mossy group of forest types, covering altitude levels from 400 to 600 m, located on gentle terrains and slopes of up to 15 degrees. The greatest vulnerability to phytophage impacts was characterized as areas located at altitudes from 400 to 600 m. The upper limit of D. sibiricus distribution was 900 m above sea level. The results obtained provide comprehensive information on the Siberian silk moth potential reserves within the study area with the possibility of extrapolation to similar territories. The data will make it possible to model pest outbreaks based on orography and improve the forest pathological monitoring methods at the regional level.

The present study attempts to identify the biological characteristics of invasive (high-impact in the secondary area) bark beetles and borers species, contributing to their success in an invaded area. We selected 42 species based on the CABI website data on invasive species and information on the most studied regional faunas. Four groups of species with different invasion strategies were identified based on the cluster and factor analysis. The first one (inbred strategy) is characterized by flightless males, xylomycetophagy, low fecundity (~50 eggs), inbreeding, polyvoltinism, and polyphagy. Species with an aggressive strategy are poly- or monovoltine, feeds on a limited number of hosts, larval feeding on the inner bark, are often associated with phytopathogens, and produce aggregation pheromones. Representatives of the polyphagous strategy have a wide range of hosts, high fecundity (~150 eggs), larval feeding on wood, and their life cycle is at least a year long. For the intermediate strategy, the typical life cycle is from a year or less, medium fecundity, feed on inner bark tissues, mono- or oligophagy. Comparison with low-impact alien species showed that the most significant traits from the viewpoint of the potential danger of native plant species are high fecundity, polyvoltinism, presence of symbiotic plant pathogens, long-range or aggregation pheromones.

The Siberian silkmoth is one of the most dangerous coniferous forests pests. Siberian silkmoth outbreaks cause massive defoliation and subsequent forest fires over vast areas. Remote forest disturbance assessments performed after an outbreak make it possible to assess carbon emissions and the potential for natural regeneration, estimate forest fire danger, and reveal the need to implement forest management practices. The goal of the present research was to investigate the use of modern satellite imagery of medium spatial resolution to estimate the percentage of dead trees in a given area. The subject of this study is the Siberian silkmoth outbreak that occurred in 2018–2020 and covered 42 thousand ha in the Irbey region of the Krasnoyarsk Krai. Imagery from the Sentinel-2/MSI sensor was used to calculate a number of spectral indices for images received before and after the outbreak. Field study data were used to create regression models relating the index values to the percentage of dead trees. A number of spectral indices, such as NDVI, dNDVI, NBR, dNBR, NDMI, EVI, and TCG, were used. As a result, spectral indices based on the data from NIR/SWIR bands (NBR, NDMI, dNBR) demonstrated the best correlations with field-measured tree mortality. Therefore, these indices may be used to accurately estimate the percentage of dead trees by remote sensing data. The best was the NBR index with an R² equal to 0.87, and the lowest RMSE and MAE errors. Consequently, Sentinel-2 imagery can be successfully used for tree mortality assessment over large inaccessible areas disturbed by Siberian silkmoth outbreaks at a relatively low cost.

The characteristics of Abies sibirica Ledeb.- and Pinus sibirica Du Tour dominated forests stands in outbreak spots formed during a Dendrolimus sibiricus outbreak in 2014–2017 were studied at the stage of population collapse (east of the West Siberian Plain and western foothills of the Yenisei Range). The research was based on the data obtained during ground surveys conducted in 2016 when stands characteristics, the Siberian silk moth population density, and defoliation level were recorded. We classified the studied stands using decision trees and random forest algorithms to identify the key characteristics that determine the formation of outbreak spots. The classification results showed that the characteristics of the detected outbreak spots differ significantly from those previously described for dark coniferous stands of the southern taiga in Siberia. The highest probability of the outbreak spot occurrence in the study area was revealed for stands with the following characteristics: moderately moist site; Siberian stone pine and Siberian fir take 40% or more of stem volume; age of less than 105 years. Another group of forest stands under threat are those sharing the following characteristics: large area (more than 60 ha); stand homogeneity; ground vegetation is dominated by feather mosses; age of more than 120 years; Picea obovata Ledeb. takes a significant share in a stem volume. Such characteristics indicate that during the population collapse, the Siberian silk moth forms outbreak spots in stands that either undergo an early successional stage after previous outbreak or have been slightly damaged during the previous outbreaks due to unfavorable habitat conditions.

In the southern taiga of Siberia, periodic outbreaks of the Siberian moth Dendrolimus sibrircus Tschetv. have been observed. The outbreaks result in the defoliation of Siberian fir Abies sibirica Ledeb. and Siberian pine Pinus sibirica Du Tour. stands across approximately one million hectares, leading to dieback of the affected forests. This is largely attributable to the inability to promptly identify the onset of the pest population growth in a timely manner, particularly in the context of expansive forest areas with limited accessibility. It is feasible to enhance the efficacy of monitoring Siberian moth populations by discerning stands with the highest propensity for damage and concentrating efforts on these areas. To achieve this, we employed machine learning techniques, specifically gradient boosting, support vector machines, and decision trees, training models on two sets of predictors. One of the datasets was obtained through a field study conducted in forest stands during the previous outbreak of the Siberian moth (2015–2018), while the other was derived from the analysis of remote sensing data during the same period. In both 2015 and 2016, the defoliation was most accurately predicted using gradient boosting (XGB algorithm), with ROC-AUC values reaching 0.89–0.94. The most significant predictors derived from the ground data were the proportions of Siberian fir, Siberian spruce Picea obovata Ledeb., and Scots pine Pinus sylvestris L., phytosociological data, tree age, and site quality. Among the predictors obtained from the analysis of remote sensing data, the distance to disturbed forest stands was identified as the most significant, while the proportion of dark coniferous species (A. sibirica, P. sibirica, or Picea obovata Ledeb.), the influx of solar radiation (estimated through the CHILI index), and the position in the relief (mTPI index) were also determined to be important.

Air pollution is the major anthropogenic factor in urban and industrial areas. Forests play the most important role in improving environmental quality in such areas. Long-term air pollution has an adverse effect on the entire biota in such forests and determines the nature of plant–pathogen interactions. The purpose of the present research was to study the pathological status of the understory in Pinus sylvestris L. stands subject to long-term anthropogenic air pollution. The study was conducted in the pine forests near Krasnoyarsk. Research methods included a detailed pathological inspection of the understory (including saplings and self-sown trees) in stands (mainly forest-steppe pine forests) growing from 10 to 30 km from the city and macroscopic and microscopic diagnostics of plant diseases, and the analysis of the data obtained was carried out using statistical criteria (Kolmogorov–Smirnov test (d_K-S), Student’s t-test). Pathogens affecting young pines (nine species identified) are micromycetes of different parasitic strategies (semi-parasites predominate). The most common pathogens are Lophodermella sulcigena (causes needle cast), Cenangium ferruginosum (necrosis of branches), and Sarea difformis (stem and branches canker). The pathogens causing necrosis and canker are the most harmful for pine understory. As young plants mature, pathogen diversity and disease manifestations increase. In stands closer to the city, understory infestations with dominant diseases significantly decrease as the degree of their contamination with heavy metals and fluorine increases. The activity of pathogenic micromycetes is likely to be suppressed by the chemicals found in the plants.