Irreversible impacts of heat on the emissions of monoterpenes, sesquiterpenes, phenolic BVOC and green leaf volatiles from several tree species
Abstract. Climate change will induce extended heat waves to parts of the vegetation more frequently. High temperatures may act as stress (thermal stress) on plants changing emissions of biogenic volatile organic compounds (BVOCs). As BVOCs impact the atmospheric oxidation cycle and aerosol formation, it is important to explore possible alterations of BVOC emissions under high temperature conditions. Applying heat to European beech, Palestine oak, Scots pine, and Norway spruce in a laboratory setup either caused the well-known exponential increases of BVOC emissions or induced irreversible changes of BVOC emissions. Considering only irreversible changes of BVOC emissions as stress impacts, we found that high temperatures decreased the de novo emissions of monoterpenes, sesquiterpenes and phenolic BVOC. This behaviour was independent of the tree species and whether the de novo emissions were constitutive or induced by biotic stress.
In contrast, application of thermal stress to conifers amplified the release of monoterpenes stored in resin ducts of conifers and induced emissions of green leaf volatiles. In particular during insect attack on conifers, the plants showed de novo emissions of sesquiterpenes and phenolic BVOCs, which exceeded constitutive monoterpene emissions from pools. The heat-induced decrease of de novo emissions was larger than the increased monoterpene release caused by damage of resin ducts. For insect-infested conifers the net effect of thermal stress on BVOC emissions could be an overall decrease.
Global change-induced heat waves may put hard thermal stress on plants. If so, we project that BVOC emissions increase is more than predicted by models only in areas predominantly covered with conifers that do not emit high amounts of sesquiterpenes and phenolic BVOCs. Otherwise overall effects of high temperature stress will be lower increases of BVOC emissions than predicted by algorithms that do not consider stress impacts.
