Adaptive cluster sampling in inventorying forest damage by the common pine sawfly (Diprion pini).
Climate change and biological invasions are threats to healthy forest environments throughout the world. Some species that have previously caused only small-scale damage have now become serious pests, causing massive outbreaks and yield losses including in Scandinavia. The spatial scale of outbreaks and intensity of defoliation caused by the common pine sawfly (Diprion pini L.) can vary between years, due to fluctuation in population dynamics. The study area is situated in Ilomantsi, eastern Finland, where D. pini has caused vast needle losses in managed Scots pine stands. We aimed at developing an accurate and cost-efficient inventory method for insect damage, in which we compared stratified adaptive cluster sampling, random adaptive cluster sampling and simple random sampling. Stratified adaptive cluster sampling proved to be the most accurate method and was a promising candidate for inventorying and monitoring pest insect damage in the study. Adaptive cluster sampling is a promising method for inventorying and monitoring such phenomena when area does not remain constant all the time.