Scot pine (Pinus sylvestris L.) is one the main forest-forming tree species in Russia, and is of great ecological, economic and social value. In order to manage the genetic resources of this important forest tree species efficiently, knowledge of its genetic diversity and structure is needed. Here, we assessed the genetic variation of 17 populations representing different parts of Scots pine range in Russia by using nuclear microsatellite markers (nSSR). Specifically, 14 populations were chosen within the natural distribution range of the species in Middle Siberia and three distant populations were sampled from the European part of Russia, the West Siberia and the Russian Far East. All populations showed high values of genetic diversity (H_E = 0.514) and A_R = 4.150. However, the easternmost population has shown the lowest level of genetic diversity (H_E = 0.433) and allelic richness (A_R = 3.505). Five genetic groups could be detected that correspond to: the European part of Russia, the south of Middle Siberia, the northwest of Middle Siberia, West and Middle Siberia, and the Russian Far East. However, the European population was the most genetically distinct one. The variation among Scots pine populations accounted for only 5% of the total variance. The highest level of genetic differentiation was found only between westernmost and easternmost populations (F_ST = 0.097). Our genetic data may contribute to a better understanding of the pattern of genetic diversity of Scots pine populations in Middle Siberia and help the conservation efforts of these genetic resources.

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.

Siberian stone pine (Pinus sibirica Du Tour) is a key component of the Eurasian boreal forest ecosystems. However, due to the ongoing climatic changes and anthropogenic activities, the habitats of the species are constantly degrading and reducing. To these reasons, exploring the genetic resources of the species and determining the genetic diversity and structure of today’s populations is essential. In this study, we assessed genetic diversity and differentiation in six Siberian stone pine populations from different forest zones in Middle Siberia. Based on seven microsatellite nuclear markers (nSSR), moderate level of genetic diversity (He=0.455) was detected. A population structure analysis divided the six Siberian stone pine populations into two groups. Southernmost populations were distinguished from the others. Analysis of molecular variance (AMOVA) showed that only 2 % of the genetic variation occurred among populations. Our findings suggest that extensive gene flow may prevent genetic differentiation among Siberian stone pine populations. Hence, further genetic diversity estimation with additional loci is needed for crucial insight into the gene pool of Siberian stone pine populations.

Background. The degradation of coniferous forests dominated by siberian fir (Abies sibirica Ledeb.) as a consequence of outbreaks of the Siberian silkmoth (Dendrolimus sibiricus Tschetv.) gives rise to considerable environmental and economic damage at the regional level. An improved understanding of the ecology of the pest population in mountainous terrain will facilitate the development of a more effective monitoring system and the use of a digital terrain model to predict the spread of the outbreak. This will allow the implementation of timely active forest protection measures. Purpose. This study aims to investigate the spatiotemporal patterns of the Siberian silkmoth outbreak by analyzing orographic data from mountainous southern taiga forests in Siberia. Materials and methods. The study was carried out by the method of retrospective analysis based on a geoinformation system combined with data from remote sensing of the Earth. The research was based on a time series of Landsat-8 images obtained from the USGS Earth Explorer data portal (https://earthexplorer.usgs.gov/) with a spatial resolution of 30 m. Results. This was achieved by analyzing time series data from Landsat-8 satellite images. An assessment was conducted of the area of defoliated forests and the relief features of such an area using the ASTER digital elevation model. This was done in order to detail the parameters for predicting the development of the pest outbreaks based on the following orographic characteristics in mid-mountain relief: height above sea level, terrain slope and slope exposure. Conclusion. It is recommended that the early detection of Siberian silkmoth outbreaks in mountainous southern taiga forests should be aimed at the initial monitoring of dark coniferous stands concentrated at altitudes from 400 to 600 m above sea level, located on flat areas and slopes of up to 15 degrees, including exposures predominantly occupied by feather moss forest types. EDN: XLRKHE

The article is devoted to studying the siberian silkmoth ( Dendrolimus sibiricus Tschetverikov, 1908) outbreaks, which is highly relevant today. The study was aimed at improving the system of forest pathology monitoring by revealing factors contributing to forest stand resistance to the pest attacks. We analyze the habitat conditions that are favorable for the siberian silkmoth in the early stages of its development and during the outbreak in dark coniferous taiga in the mountains of Southern Siberia (particularly, the Krasnoyarsk region). The research was based on remote sensing data combined with field data obtained in disturbed forests where we also con-ducted a pest population assessment. The habitat characteristics were based on the forest type and silvicultural and forest inventory details. We an alyzed environmental conditions and their influence on the pest reproduction. As a result, we divided the forest stands by their resistance to insect attacks. Primary habitats are stands with moss-covered forest floor, secondary habitats are stands with herbs-covered forest floor, and tertiary habitats are depressions with wet soils (forests along streams or swamp forests). We revealed the D. sibiricus population density dynamics related to the forest stands structure. The present research lets us determine forest sites vulnerable to pest attacks and conduct spatial monitoring of their dynamics and resistance.

The article presents the findings of field studies conducted in coniferous forests that were subjected to mass defoliation by caterpillars of the Siberian moth between 2015 and 2018 in the taiga zone. The loss of tree crowns by more than 75% resulted in the complete loss of functional sustainability and a degradation of dark coniferous stands (stands dominated by Siberian fir, Siberian pine and Siberian spruce). The study yielded findings nine years after the outbreak began, including an assessment of the forest stand's current state, the mortality rate, and the volume of deadwood. The succession scenario encompasses a series of stages, including the natural restoration of the forest through the change of species (from coniferous to deciduous trees) and active swamping processes. Consequently, for an extended period, the study area will continue to present a significant risk in terms of fire hazard and environmental degradation.