Related Experiment Video
Updated: Feb 10, 2026

Study of Siphon Breaker Experiment and Simulation for a Research Reactor
Published on: September 26, 2017
Resistance of native oak to recurrent drought conditions simulating predicted climatic changes in the Mediterranean
Amélie Saunier1,2, Elena Ormeño1, Michel Havaux3
1Aix-Marseille University, Avignon University, CNRS, IRD, IMBE, Marseille, France.
Abstract:
The capacity of a Quercus pubescens forest to resist recurrent drought was assessed on an in situ experimental platform through the measurement of a large set of traits (ecophysiological and metabolic) studied under natural drought (ND) and amplified drought (AD) induced by partial rain exclusion. This study was performed during the third and fourth years of AD, which correspond to conditions of moderate AD in 2014 and harsher AD in 2015, respectively. Although water potential (Ψ) and net photosynthesis (Pn) were noticeably reduced under AD in 2015 compared to ND, trees showed similar growth and no oxidative stress. The absence of oxidative damage could be due to a strong accumulation of α-tocopherol, suggesting that this compound is a major component of the Q. pubescens antioxidant system. Other antioxidants were rather stable under AD in 2014, but slight changes started to be observed in 2015 (carotenoids and isoprene) due to harsher conditions. Our results indicate that Q. pubescens could be able to cope with AD, for at least 4 years, likely due to its antioxidant system. However, growth decrease was observed during the fifth year (2016) of AD, suggesting that this resistance could be threatened over longer periods of recurrent drought.
Related Concept Videos
Responses to Drought and Flooding
Global Climate Change
What is Climate?
Predicting Molecular Geometry
IR Frequency Region: Fingerprint Region
Prediction Intervals
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y.

