# Current Problems of Forest Protection (25-27 October 2022, Katowice Poland)
## 11 **Intermediate-Term Forecasting Nun Meth Outbreaks**  
Lidia Sukovata 1 and Wiktor Tracz 2  
Footnote 1: Forrest Research Institute, Sekocin Stary, Poland;  
Footnote 2: Warsaw University of Life Sciences, Warsaw, Poland  
Footnote *: Correspondence: L.Soukovata@ibles.waw.pl  
**Abstract**: The ability to predict the timing, location and extent of insect pest outbreaks is of great importance for strategic forest management planning. Short-term (up to 1 year) predictions are most common and are based on population densities at the pupal, adult or egg stage. The development of intermediate- (future 1-5 years) and long-term (more than 5 years) prediction models is usually based on historical data, and the longer the time series, the greater the chance of obtaining a good fit of model predictions to empirical data. The nun moth (NM) _Lymantria monachu_ (L.) (Lepidoptera, Erebidae) is one of the most serious pests of Scots pine forests in Poland and its outbreaks usually cover a large area of the country. Data on outbreaks have been collected since the mid-1950s. They include: (a) pest occurrence in year t-1; (b) predicted threat at warning, low, medium and high levels in year t; and (c) pest control with insecticides in year t-1 at the forest district level. The availability of such data provides the basis for developing predictive models, including intermediate-term models. As a first step towards developing comprehensive models for forecasting timing and extent of NM outbreaks, we focused on predicting outbreak onset at the regional level; i.e., for each of the 17 Regional Directorates of State Forests (RDSF) in Poland. This could serve as a warning signal for foresters to pay more attention when assessing insect population density. The onset of an insect outbreak was considered during a year when a medium and/or high threat to forests was first recorded after the previous outbreak. Meteorological conditions were assumed to be among the most important exogenous factors leading to insect outbreaks. Logistic regression was used to analyze the relationships between the onset of the NM outbreaks and 168 meteorological variables with time lags t-2, t-3 and t-4. As a result, 16 logit models were developed for all but one RDSF, where the NM outbreak was never recorded. In the next step, we focused on developing models using Random Forest to predict the onset of outbreaks at the level of groups of forest districts formed by considering their proximity to the nearest meteorological station and the similarity of NM outbreak history. The results of intermediate-term predictions at different levels were compared with the short-term predictions (less than 1 year) made by foresters according to standard protocols. **Keywords:**_Lymantria monacha_; Lepidoptera; outbreak predictions; logistic and random forest models; meteorological data +
Footnote †: journal: Journal of Computational and Graphical Statistics  
**Flight Dynamics of Great Pine Web-Spinning Pine Sawfly _Acantholyda posticalis_ (MATS.) Using Traps in Three Regions of Increased Occurrence**  
Tomasz Mokrzycki  
Warsaw University of Life Sciences, Warsaw, Poland  
\({}^{\star}\) Correspondence: tomasz_mokrzycki@sggw.edu.pl  
**Abstract:**_Acantholyda posticalis_ in Poland is classified as a dangerous, primary pest of _Pinus sylvestris_. Monitoring its population is of great importance for making decisions about controlling this insect and protecting forest stands. The aim of the study was to investigate the influence of air temperature on the flight dynamics of _Acantholyda posticalis_ and the influence of the location of the trap on the accuracy of monitoring. The effect of population location on insect size was also studied. Observations of _Acantholyda posticalis_ were carried out in 2020 and 2021 from mid-March to the end of August in the following Kolumina, Lublinice and Przedborow forest districts. On each plot, 12 \"Geolas\" traps were placed on pine trees. Three loggers were placed on each research plot to measure air temperature.