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The biology and population dynamics of pine sawfly Diprion pini L. are extremely complex and variable. Among other factors, climatic conditions determine the potential for mass outbreaks of the species. In this paper, we investigate this influence and describe a statistical approach to identify responsible climatic variables in floating time windows, thus identifying the factors responsible for the transition from latency to outbreak events. Analyses were built upon a data base comprising outbreak events and fine-scaled climatic data for the period 2002–2016 for a model region in the state of Brandenburg, Germany. By applying Random Forest statistic classification analyses, we isolated a set of four variables. They cover precipitation, temperature, and potential evapotranspiration in distinct periods during the current and the previous year. These periods are not fixed in their position but attached to the floating phenological date of bud burst of the host species Pinus sylvestris L. The complete set of variables was able to distinguish forests likely to be defoliated from those not threatened at high probabilities (95% true-positive rate, 98% true-negative rate). Theidentified climatic windows offer insights into population dynamics in the study region, support adjustments in current monitoring algorithms, and indicate starting points for further investigations covering other regions or different years

Improved Outbreak Prediction for Common Pine Sawfly (Diprion pini L.) by Analyzing Floating ‘Climatic Windows’ as Keys for Changes in Voltinism