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We tested the seasonal variability of frost resistance and photosynthesis characteristics, namely net photosynthetic rate at saturating irradiance and ambient atmospheric CO2 concentrations, maximum carboxylation rate, and measurements based on the JIP-test, namely maximum quantum yield of PSII and the performance index. Additionally, we measured leaf functional traits, namely specific leaf area, leaf dry matter content, stomatal pore area index, and chlorophyll content. We assessed all traits weekly on two evergreen species Berberis vulgaris and Quercus × hispanica as well as the two summer green species Betula pendula and Quercus pubescens. We found that there was a high seasonal variability in all traits except stomatal pore area index. In a multivariate analysis, we could show trade-offs between growth rates and leaf thickness on the first axis, as well as resistance and performance on the second axis. The findings help understand ecophysiological processes and fine-tune biosphere models.

We tested whether cheap and quick chlorophyll (Chl) fluorescence can be used in ecophysiological field studies as proxies for gas-exchange measurements. We measured net photosynthetic rate at saturating irradiance and ambient atmospheric CO2 concentrations (PNsat), maximum carboxylation rate (Vcmax), maximum quantum yield of PSII (Fv/Fm), the performance index (PIabs), leaf nitrogen (Narea), and carbon isotope discrimination (Δ13C) within four herbaceous species along two elevational gradients. We analysed the relationship between Chl fluorescence and gas-exchange parameters and their link to indirect assessment of plant performance via ecophysiological traits. Fv/Fm showed no relationship to PNsat and only weak relationships to Vcmax. PIabs was positively related to PNsat and Vcmax. PIabs, PNsat, and Vcmax were positively associated with Narea and negatively to Δ13C, whereas Fv/Fm showed no relationship to Narea and a positive to Δ13C. Thus, PIabs might be suitable to characterize the photosynthetic activity when aiming on large numbers of samples.