Siberian Moth Research Articles

Changes in Spatiotemporal Pattern and Its Driving Factors of Suburban Forest Defoliating Pest Disasters

Forest defoliating pests are significant global forest disturbance agents, posing substantial threats to forest ecosystems. However, previous studies have lacked systematic analyses of the continuous spatiotemporal distribution characteristics over a complete 3–5 year disaster cycle based on remote sensing data. This study focuses on the Dendrolimus superans outbreak in the Changbai Mountain region of northeastern China. Utilizing leaf area index (LAI) data derived from Sentinel-2A satellite images, we analyze the extent and dynamic changes of forest defoliation. We comprehensively examine the spatiotemporal patterns of forest defoliating pest disasters and their development trends across different forest types. Using the geographical detector method, we quantify the main influencing factors and their interactions, revealing the differential impacts of various factors during different growth stages of the pests. The results show that in the early stage of the Dendrolimus superans outbreak, the affected area is extensive but with mild severity, with newly affected areas being 23 times larger than during non-outbreak periods. In the pre-hibernation stage, the affected areas are smaller but more severe, with a cumulative area reaching up to 8213 hectares. The spatial diffusion characteristics of the outbreak follow a sequential pattern across forest types: Larix olgensis, Pinus sylvestris var. mongolica, Picea koraiensis, and Pinus koraiensis. The most significant influencing factor during the pest development phase was the relative humidity of the year preceding the outbreak, with a q-value of 0.27. During the mitigation phase, summer precipitation was the most influential factor, with a q-value of 0.12. The combined effect of humidity and the low temperatures of 2020 had the most significant impact on both the development and mitigation stages of the outbreak. This study’s methodology achieves a high-precision quantitative inversion of long-term disaster spatial characteristics, providing new perspectives and tools for real-time monitoring and differentiated control of forest pest infestations.

Microsporidia-cypovirus interactions during simultaneous infection of the tree defoliator Dendrolimus sibiricus (Lepidoptera: Lasiocampidae)

The Siberian moth, Dendrolimus sibiricus is a dangerous forest defoliator, the number one pest of boreal forests in Asia. Search for effective and ecologically friendly control measures drives attention to microbial pathogens. Viruses and microsporidia are obligate intracellular parasites widespread in insect populations causing either chronic or acute infections. Interactions of these pathogens vary from antagonistic to synergistic. The goal of the work was to test a recently discovered cytoplasmatic polyhedrosis virus (cypovirus) strain DsCPV-1 isolated from D.sibiricus, combined with a microsporidium, against D. sibiricus, by feeding the inoculum (viral polyhedral and microsporidian spores). Three different microsporidian parasites of lepidopterans were tested against D. sibiricus as monoinfection: Nosema bombycis from silkworm, N. pyrausta from corn borer, and Tubulinosema loxostegi from beet webworm. Nosema bombycis was the most virulent, with a median lethal time of 7 days in the first and second instars treated with 100,000 and 1 million spores/larva, respectively. Nosema bombycis (dose 100,000 spores/larva) was chosen to test it as mixed infection in combination with an extremely low dose of DsCPV-1 (1 polyhedron/larva) against two races of D. sibiricus second instar larvae (the fir-feeding race and the larch-feeding race). The mixed infection demonstrated the most prominent negative effect on larval lethal time and weight for the both tested races. Mixed infections showed a synergistic effect for the fir-feeding larvae but additive effect only for the larch feeding larvae. Both pathogens co-developed successfully in the larvae with equal ratio of producing inoculum. The combination of these entomopathogens is therefore promising for forest protection against the Siberian moth and could be the way to significantly decrease the amount of pathogens applied in field.

Is It Possible to Predict a Forest Insect Outbreak? Backtesting Using Remote Sensing Data

In this study, methods are proposed for analyzing the susceptibility of forest stands to attacks by forest insects on the basis of Earth remote sensing data. As an indicator of the state of forest stands, we proposed to use a parameter of the sensitivity of a vegetation index (normalized difference vegetation index; NDVI) during a vegetative period to changes in the radiative temperature of the territory (land surface temperature; LST) determined from satellite data of the Terra/Aqua system. The indicator was calculated as a spectrum of a response function in an integral equation linking changes of NDVI to those of LST. Backtesting was carried out using data from two outbreaks of the Siberian silk moth Dendrolimus sibiricus Tschetv. and outbreaks of the white mottled sawyer Monochamus urussovi Fischer and of the four-eyed fir bark beetle Polygraphus proximus Blandford in taiga forests of Krasnoyarsk Territory in Russia. In addition, the state of fir stands in the year 2023 was examined when damage to the forest stands was not yet noticeable, but Siberian silk moth adults were found in pheromone traps. It was shown that the proposed indicator of susceptibility of forest stands changed significantly 2–3 years before the pest outbreak in outbreak foci of the studied areas. Thus, the proposed indicator can be used to predict outbreaks of insect pests. The proposed approach differs from commonly used remote sensing methods in that, rather than using absolute values of remote indicators (such as, for example, NDVI), it focuses on indicators of the susceptibility of these remote indicators to the characteristics of the natural environment. Since any given point on the planet is characterized by a seasonally varying temperature, it is always possible to determine the sensitivity of a remote sensing indicator to changes in the environment that are not directly related to the absolute value of the indicator. Future studies are expected to examine susceptibility indices as a function of forest stand location and species, and to examine the length of spatial correlation of susceptibility indices, which may provide information on the possible extent of future insect outbreaks.

Assessment and Forecasting of Phytosanitary Risks in the Forests of the Irkutsk Region

Introduction. Timber export plays a significant role in the budget of the Irkutsk region. To ensure the continued and sustainable use of forest resources, it is essential to implement preventive measures for forest conservation. One such measure is the analysis of phytosanitary risk, which helps to identify potentially harmful insects and determine the likelihood of their introduction and spread, as well as the potential economic consequences. From the perspective of applied riskology, it is necessary to carry out a predictive assessment, calculate the acceptability of risks and develop methods for managing them, combining economic and monitoring approaches. The aim of the presented work was to assess and predict phytosanitary risks in the Irkutsk region and potential damage to forestry and the economy, as well as to develop measures to reduce them.Materials and Methods. For this analysis, we used the results of forest surveys conducted in the Irkutsk region in 2021–2023 with the participation of the authors of this article. These surveys included the identification and detection of harmful insects, as well as the determination of their distribution areas according to GOST 34 309–2017 and the methodology approved by the phytosanitary control authority. Additionally, data from official statistics from the Federal Customs Service of Russia for 2021–2023 were used.Results. We found populations of quarantine pests listed in the Unified List of Quarantine Objects of the Eurasian Economic Union in the forests of Ust-Ilimsky district, such as Monochamus sutor, Monochamus sartor, Monochamus galloprovincialis, Dendrolimus superans. We calculated the phytosanitary risk and assessed the quarantine phytosanitary zone, taking into account the buffer zone.Discussion and Conclusion. The results of the analysis suggest an unfavorable phytosanitary situation in the studied areas. The high infestation of the detected harmful insects in the Ust-Ilimsky district compared to the reference areas indicates the potential for quarantine zones and losses for loggers. To manage phytosanitary risks, it is important to select options that are effective in reducing the spread of quarantine organisms and minimizing risks to an acceptable level. Sanitary logging with timely removal of wind-damaged and fire-affected trees, as well as the use of pheromone traps and biological products, are environmentally friendly options for managing phytosanitary risks.

Forecasting Dendrolimus sibiricus Outbreaks: Data Analysis and Genetic Programming-Based Predictive Modeling

This study presents an approach to forecast outbreaks of Dendrolimus sibiricus, a significant pest affecting taiga ecosystems. Leveraging comprehensive datasets encompassing climatic variables and forest attributes from 15,000 taiga parcels in the Krasnoyarsk Krai region, we employ genetic programming-based predictive modeling. Our methodology utilizes Random Forest algorithm to develop robust forecasting model through integrated data analysis techniques. By optimizing hyperparameters within the predictive model, we achieved heightened accuracy, reaching a maximum precision of 0.9941 in forecasting pest outbreaks up to one year in advance.

Ecological characteristics of the siberian silkmoth (Dendrolimus sibiricus) (Insecta, Lasiocampidae) habitat in the Sayan montane conifer forests ecoregion

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.

Evaluating Dendrolimus superans (Lepidoptera: Lasiocampidae) Occurrence and Density Modeling with Habitat Conditions

Dendrolimus superans, a prominent forest pest in northeast China, exerts detrimental effects on tree growth and development, disrupts the ecological functioning of forests, and even alters the trajectory of succession. The objective of this study was to investigate the influence of habitat conditions on the occurrence probability and density of overwintering D. superans, aiming to provide scientific insights for the effective prevention of and control measures against this pest infestation. The investigation encompassed 142 plots (20 m × 20 m) in various forest types within the primary distribution area of D. superans in the Great Xing’ an Mountains, focusing on factors such as topography, forest vegetation, and larval density. Binary logistic regression was employed to establish models for predicting the occurrence probability of D. superans, while generalized linear models (GLMs) and categorical regression (CATREG) were utilized to develop models for estimating its population size. Subsequently, an evaluation was conducted to assess the performance of these models. The occurrence probability model showed high accuracy (AUC = 0.826) in predicting infestation. The slope aspect and herb cover were the key factors affecting the occurrence of D. superans. The occurrence probability was the lowest on shady slopes and the highest on sunny slopes. The occurrence probability of D. superans increased with the increase in herb cover. The model of quantification showed that the density of D. superans was the least on shady slopes and the highest on sunny slopes. As the slope gradient increased, the density decreased. D. superans occurred most frequently on ridges. Similarly, with the increase in canopy cover or the decrease in diameter at breast height (DBH) and stand density, the density of D. superans increased. The influence of the topography factors surpassed that of the forest vegetation factors in shaping the population dynamics of D. superans, despite both being significant contributors. The study revealed that D. superans is prone to occur on sunny slopes, flat slopes, and ridges, which should be the focus of prevention and control in forest management practices, such as replanting, thinning, and regular weeding, to help restrain the growth of the population of this pest.

The Diversity of Parasitoids and Their Role in the Control of the Siberian Moth, Dendrolimus sibiricus (Lepidoptera: Lasiocampidae), a Major Coniferous Pest in Northern Asia.

The Siberian moth, Dendrolimus sibiricus Tschetv., 1908 (Lepidoptera: Lasiocampidae) is a conifer pest that causes unprecedented forest mortality in Northern Asia, leading to enormous ecological and economic losses. This is the first study summarizing data on the parasitoid diversity and parasitism of this pest over the last 118 years (1905-2022). Based on 860 specimens of freshly reared and archival parasitoids, 16 species from two orders (Hymenoptera and Diptera) were identified morphologically and/or with the use of DNA barcoding. For all of them, data on distribution and hosts and images of parasitoid adults are provided. Among them, the braconid species, Meteorus versicolor (Wesmael, 1835), was documented as a parasitoid of D. sibiricus for the first time. The eastern Palaearctic form, Aleiodes esenbeckii (Hartig, 1838) dendrolimi (Matsumura, 1926), status nov., was resurrected from synonymy as a valid subspecies, and a key for its differentiation from the western Palaearctic subspecies Aleiodes esenbeckii ssp. esenbecki is provided. DNA barcodes of 11 parasitoid species from Siberia, i.e., nine hymenopterans and two dipterans, represented novel records and can be used for accurate molecular genetic identification of species. An exhaustive checklist of parasitoids accounting for 93 species associated with D. sibirisus in northern Asia was compiled. Finally, the literature and original data on parasitism in D. sibiricus populations for the last 83 years (1940-2022) were analysed taking into account the pest population dynamics (i.e., growth, outbreak, decline, and depression phases). A gradual time-lagged increase in egg and pupal parasitism in D. sibiricus populations was detected, with a peak in the pest decline phase. According to long-term observations, the following species are able to cause significant mortality of D. sibiricus in Northern Asia: the hymenopteran egg parasitoids Telenomus tetratomus and Ooencyrtus pinicolus; the larval parasitoids Aleiodes esenbeckii sp. dendrolimi, Cotesia spp., and Glyptapanteles liparidis; and the dipteran pupal parasitoids Masicera sphingivora, Tachina sp., and Blepharipa sp. Their potential should be further explored in order to develop biocontrol programs for this important forest pest.

Identification of Larch Caterpillar Infestation Severity Based on Unmanned Aerial Vehicle Multispectral and LiDAR Features

Utilizing UAV remote sensing technology to acquire information on forest pests is a crucial technical method for determining the health of forest trees. Achieving efficient and precise pest identification has been a major research focus in this field. In this study, Dendrolimus superans (Butler) was used as the research object to acquire UAV multispectral, LiDAR, and ground-measured data for extracting sensitive features using ANOVA and constructing a severity-recognizing model with the help of random forest (RF) and support vector machine (SVM) models. Sixteen sensitive feature sets (including multispectral vegetation indices and LiDAR features) were selected for training the recognizing model, of which the normalized differential greenness index (NDGI) and 25% height percentile were the most sensitive and could be used as important features for recognizing larch caterpillar infestations. The model results show that the highest accuracy is SVMVI+LIDAR (OA = 95.8%), followed by SVMVI, and the worst accuracy is RFLIDAR. For identifying healthy, mild, and severely infested canopies, the SVMVI+LIDAR model achieved 90%–100% for both PA and UA. The optimal model chosen to map the spatial distribution of severity at the single-plant scale in the experimental area demonstrated that the severity intensified with decreasing elevation, especially from 748–758 m. This study demonstrates a high-precision identification method of larch caterpillar infestation severity and provides an efficient and accurate data reference for intelligent forest management.

Algorithm for predicting Siberian silkmoth outbreaks in taiga forests of Krasnoyarsk Krai

Forests disturbances by pests and diseases remains one of the major problems for forestry. These factors, combined with logging, fires and other human impacts, lead to degradation of forest ecosystems. Nowadays, forest health in Russia is assessed using forest inventory data and state forest health monitoring data. At the beginning of the 2020s, the Siberian silkmoth (Dendrolimus superans sibiricus Tscetv.) population continues to rise and damage taiga forests. The present study is dedicated to one of the methods for improving forest health monitoring. The method is based on remote sensing (in order to cover large forest areas), combined with GIS-based forecast models. The models make it possible to predict the risk of the Siberian silkmoth outbreak based on previously studied dependences between the pest population characteristics and environmental factors.

The impact of quarantine pests on forest health in Krasnoyarsk krai

The present study examines the dynamics of the areas experiencing outbreaks of pests classified as "quarantine" in Krasnoyarsk Krai between 2007 and 2023. It can be observed that the forest's health is deteriorating year on year, as the area affected by these pests increases. The coniferous forests of Krasnoyarsk Krai are most at risk from the Siberian moth and the four-eyed fir bark beetle, which cause significant disturbances to forest ecosystems, resulting in substantial environmental and economic damage. There are several shortcomings in the forest management system with regard to forest protection. These include the lack of effective methodologies for assessing the state of forests following extensive damage by pests, as well as the limitation of monitoring information received in the region.

Habitat modeling and future trends for the outbreak of the Siberian moth (Dendrolimus sibiricus Tschetv.) in Mongolia

Habitat modeling and future trends for the outbreak of the Siberian moth (Dendrolimus sibiricus Tschetv.) in Mongolia

Forest Insect Outbreak Dynamics: Fractal Properties, Viscous Fingers, and Holographic Principle

During the growth of a forest insect outbreak epicenter, there are processes that involve the formation and expansion of the primary epicenter of forest damage, as well as secondary epicenters—both connected and unconnected to the primary one. This study characterizes outbreak epicenters in terms of their fractal dimensions and “viscous finger” parameters at the epicenter boundary, highlighting their significance in the context of forest insect management. Local outbreak epicenters were found to be characterized by fractal dimension D = 1.4–1.5, and the boundaries of the epicenters were described using the “viscous finger” model. Proposed models were constructed and validated using remote sensing data obtained from MODIS and Sentinel-2 satellites at epicenter sites and boundaries during the outbreak of the Siberian silk moth Dendrolimus sibiricus Tschetverikov from 2014 to 2020 in the Krasnoyarsk region of Russia. The study revealed that the frequency of the mode spectrum of one-stage spatial series of “viscous fingers” corresponds with the data on the development of the outbreak foci area.

Monitoring and Prediction of Siberian Silk Moth Dendrolimus sibiricus Tschetv. (Lepidoptera: Lasiocampidae) Outbreaks Using Remote Sensing Techniques.

The feasibility of risk assessment of a Siberian silk moth (Dendrolimus sibiricus Tschetv.) outbreak was analyzed by means of landscape and weather characteristics and tree condition parameters. Difficulties in detecting forest pest outbreaks (especially in Siberian conditions) are associated with the inability to conduct regular ground surveillance in taiga territories, which generally occupy more than 2 million km2. Our analysis of characteristics of Siberian silk moth outbreak zones under mountainous taiga conditions showed that it is possible to distinguish an altitudinal belt between 400 and 800 m above sea level where an outbreak develops and trees are damaged. It was found that to assess the resistance of forest stands to pest attacks, researchers can employ new parameters: namely, characteristics of a response of remote sensing variables to changes in land surface temperature. Using these parameters, it is possible to identify in advance (2-3 years before an outbreak) forest stands that are not resistant to the pest. Thus, field studies in difficult-to-access taiga forests are not needed to determine these parameters, and hence the task of monitoring outbreaks of forest insects is simplified substantially.

Mapping Tree Mortality Caused by Siberian Silkmoth Outbreak Using Sentinel-2 Remote Sensing Data

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.

Predictors of Insect Damage to Forest Stands According to Satellite Data Using the Siberian Silkmoth Dendrolimis Sibiricus Tschetv as an Example

Predictors of Insect Damage to Forest Stands According to Satellite Data Using the Siberian Silkmoth Dendrolimis Sibiricus Tschetv as an Example

The Impact of a Cypovirus on Parental and Filial Generations of Lymantria dispar L.

Recently, we found that the spongy moth Lymantria dispar L. is susceptible to infection by a Dendrolimus sibiricus cytoplasmic polyhedrosis virus (DsCPV-1). In the present study, we evaluated the pathogenicity of DsCPV-1 against L. dispar larvae and its impact on surviving insects after the infection. Offspring of virally challenged insects were tested for susceptibility to a stress factor (starvation). In addition, we used light microscopy and quantitative polymerase chain reaction (qPCR) to test the ability of DsCPV-1 to be transmitted vertically. We found insect mortality of the L. dispar parents following the infection was positively associated with DsCPV-1 dose. DsCPV-1 was lethal to second-instar L. dispar larvae with a 50% lethal dose (LD50) of 1687 occlusion bodies per larva. No vertical transmission of DsCPV-1 to offspring larvae was detected, while the majority of insect deaths among offspring larvae were caused by microsporidia (Vairimorpha lymantriae), which was harbored by the parents. The offspring of virally challenged parents exhibited a higher number of detected microsporidia compared to the control. Our findings suggest that the application of DsCPV-1 is effective in controlling pests in terms of transgenerational impact following virus exposure.

Autoregression, First Order Phase Transition, and Stochastic Resonance: A Comparison of Three Models for Forest Insect Outbreaks

Three models of abundance dynamics for forest insects that depict the development of outbreak populations were analyzed. We studied populations of the Siberian silkmoth Dendrolimus sibiricus Tschetv. in Siberia and the Far East of Russia, as well as a population of the pine looper Bupalus piniarius L. in Thuringia, Germany. The first model (autoregression) characterizes the mechanism where current population density is dependent on population densities in previous k years. The second model considers an outbreak as analogous to a first-order phase transition in physical systems and characterizes the outbreak as a transition through a potential barrier from a low-density state to a high-density state. The third model treats an outbreak as an effect of stochastic resonance influenced by a cyclical factor such as solar activity and the “noise” of weather parameters. The discussion focuses on the prediction effectiveness of abundance dynamics and outbreak development for each model.

Differentiation of Forest Stands by Susceptibility to Folivores: A Retrospective Analysis of Time Series of Annual Tree Rings with Application of the Fluctuation-Dissipation Theorem

This study analyzed the relationship between characteristics of annual tree ring time series and the intensity of attacks on forest stands by forest insects. Using tenets of the fluctuation–dissipation theorem (which is widely used in physics), time series parameters are proposed that can help to assess the susceptibility of a forest stand to insect pests. The proposed approach was applied to evaluate differences in parameters of tree ring widths among outbreaks of the pine looper, Siberian silk moth, and spongy moth. A comparison of trees characteristics between outbreak locations and undamaged forest stands (control) showed that the tested parameters statistically significantly differed between the outbreak locations and control stands and can be used to assess the risk of pest outbreaks in forest stands.

The Role of Forest Stands Characteristics on Formation of Exterior Migratory Outbreak Spots by the Siberian Silk Moth Dendrolimus sibiricus (Tschetv.) during Population Collapse

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.