Ecotron experiments reveal non-linear responses of Fagus sylvatica to realistic future climate scenarios

Plants in natural ecosystems are simultaneously exposed to multiple, interacting climate drivers, including rising temperature, vapor pressure deficit, atmospheric CO₂ and tropospheric ozone. However, most experimental studies rely on the static manipulation of a limited set of climate drivers (typically one or two), which restricts our ability to detect emergent or non-linear responses under future conditions.

Here, we synthesize results from an ecotron study conducted at the Model EcoSystem Analyser (TUMmesa). Young Fagus sylvatica trees were exposed for three growing seasons to three dynamically simulated, regionalized climate scenarios, including a control scenario (representing an average 1987-2016 climate), a mitigation scenario (RCP2.6), and a worst-case scenario (RCP8.5). The scenarios comprised realistic seasonal and diurnal co-variation of temperature, radiation, humidity, CO₂ and O₃ at hourly resolution.

Across physiological, carbon-dynamic and transcriptomic datasets, we consistently observed strong non-linear responses to increasing climate severity. While moderate future conditions (RCP2.6) induced measurable acclimation responses, plants exhibited qualitatively different responses in RCP8.5, suggesting a shift in regulatory strategies under more extreme future climates. These included threshold-like shifts in gene expression, enhanced assimilation with accelerated carbon turnover, increased belowground allocation, and altered stomatal regulation affecting transpiration and ozone uptake.

Our results demonstrate that experiments manipulating only a limited set of climate drivers, or relying on extrapolation from moderate scenarios, are insufficient to predict plant responses to future climates. Instead, realistic multivariate climate simulations in ecotrons are indispensable for capturing emergent stress responses, advancing eco-physiological understanding, and improving the reliability of process-based vegetation models under future climate change.