Temperature manipulation experiments are an effective way for testing plant responses to future climate conditions, especially for predicting shifts in plant phenological events. While passive warming techniques are widely used to elevate temperature in low stature plant communities, active warming has been applied less frequently due to the associated resource requirements. In forest ecosystems, however, active warming is crucial to simulate projected air temperature rises of 3-5 K, especially at the warm (i.e., southern and low elevation) range edges of tree species. Moreover, the warming treatment should be applied to the complete height of the experimental plants, e.g., regenerating trees in the understory. Here, we combined open top chambers (OTCs) with active heat sources, an electric heater (OTC-EH) and warming cables (OTC-WC), and tested the effectiveness of these set-ups to maintain constant temperature differences compared to ambient temperature across 18 m plots. This chamber size is needed to grow tree saplings in mixture in forest gaps for 3 to 10 years. With passive warming only, an average temperature increase of approx. 0.4 K as compared to ambient conditions was achieved depending on time of the day and weather conditions. In the actively warmed chambers, average warming exceeded ambient temperatures by 2.5 to 2.8 K and was less variable over time. However, active warming also reduced air humidity by about 15%. These results underline the need to complement passive warming with active warming in order to achieve constant temperature differences appropriate for climate change simulations under all weather conditions in large OTCs. Since we observed considerable horizontal and vertical temperature variation within OTCs with temperature differences of up to 16.9 K, it is essential to measure and report within-plot temperature distribution as well as temporal temperature variation. If temperature distributions within large OTCs are well characterized, they may be incorporated in the experimental design helping to identify non-linear or threshold responses to warming.

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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7714718PMC
http://dx.doi.org/10.3389/fpls.2020.539584DOI Listing

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