Opportunities for assessing the risk of an outbreak of Siberian silkworm (Dendrolimus superans sibiricus Tschetv.) in taiga forests

Abstract

The problem of damage and infestation of forests by pests and diseases takes a significant place in forestry. The combination of these factors, taking into account cutting and harm to forests from fires and other human activities, as a result leads to tremendous degradation of forest ecosystems. In modern conditions, the forest pathological stands condition in Russia is determined on the basis of data from forest pathological surveys and state forest pathological monitoring. For the current period (2020), disappointing statistics remain on the reproduction of the Siberian silkworm population (Dendrolimus superans sibiricus Tscetv.) nd the negative consequences for taiga forests. Modern climatic changes on a planetary scale have a significant impact on mass outbreaks of dangerous phytophage. In the near future, adaptation measures in the field of forest pathological monitoring should be based on risk factors when planning forest protection measures, a diverse selection of adaptation options and adopting a flexible forest management system at the regional level. In this paper, we consider one of the methods for improving forest pathological monitoring, based on the principles of covering as large a territory as possible with remote sensing data (ERS) combined with predictive models based on GIS that allow predicting the risk of mass reproduction of the Siberian silkworm based on the previously studied factorial dependence.

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

“…The detrimental effects of Dendrolimus sibiricus outbreaks on forest ecosystems, including reduced biodiversity, altered nutrient cycling, and increased susceptibility to other disturbances, have been well-documented in numerous research studies [11][12][13]. Additionally, the economic impact of these outbreaks [14,15], in terms of losses to timber production, forest regeneration efforts, and ecosystem services, has led to a heightened interest in understanding and predicting the occurrence of these events [16][17][18].…”