FORFUS-RT2.1

Heat and drought-related stress and mortality in forests

Inspiration

Although the mechanism by which drought and heat damages trees is poorly understood, it is known that water depletion from plant tissues and the associated low water potential values can result in a destructive embolism inside water transport tissues, hence reducing their potential to resume the water supply after the drought. To prevent such damage, trees can close their stomata when the water supply from the root system is inadequate. However, this reduces CO2 uptake and photosynthesis, potentially leading to carbon starvation over extended time periods.
Experiments suggest that water preservation is more crucial than CO2 uptake during a drought, but stomatal closure, especially during a heat wave, also carries the risk of reaching lethal leaf temperatures in sun-exposed leaves.

Innovation

The objectives of FORFUS-RT2.1 are to (a) develop methods to quantify heat and drought damage to trees, and (b) better understand how vulnerable different tree species at different sites are to hydraulic failure and heat damage during heat/drought waves. The doctoral candidate will leverage LIST's extensive network of monitoring sites and a new rain exclusion  experiment carried out in collaboration with FORFUS-RT1.2.

Thermographic infrared cameras will be installed at selected sites to record leaf temperatures, and stem dendrometers to record stem growth and water status. Stem psychrometers are available  to establish links between the stem water deficit and water potential. Monitoring activities can be complemented by both episodic leaf gas exchange measurements to assess the vitality of leaves, and the sampling of twig materials, followed by a laboratory analysis of their hydraulic conductivity and embolism degree.

Impact

The scientific knowledge generated in this project will help identify the tree species and forest types with good survival chances i n future climates in Luxembourg. The understanding and methods developed as part of the project are expected to be useful for similar studies at different sites around the world, in order to strengthen our ability to understand, anticipate and ultimately prevent drought and heat-related forest decay.

 

Research domains
  • Environment

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 Stanislaus SCHYMANSKI
Stanislaus SCHYMANSKI
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Further information

About Stanislaus Schymanski

I am a Senior Lead Research and Technology Associate at LIST, where I started a group on "Water and vegetation in a changing environment" (WAVE) in 2017, funded through an ATTRACT fellowship by the Luxembourg Research Fund (FNR). I hold a degree in Biology from the University of Freiburg (Germany) and a PhD in Environmental Engineering from the University of Western Australia. Between my PhD and current employment, I worked as a scientist at the Max Planck Instutute for Biogeochemistry in Jena (2007-2011) and the Swiss Federal Institute of Technology (ETH) in Zurich (2011-2017). Throughout my career, I have been investigating the interactions between vegetation, soil and atmosphere and the resulting hydrologic behaviour of hillslopes and catchments. In search of general laws guiding these interactions, my research focuses on physical constraints, biological adjustments and macroscopic extremum principles such as maximum net carbon profit or maximum entropy production. I combine mathematical analysis with numerical modelling to generate hypotheses and engage in lab and field observations to test these hypotheses and formulate new questions.

Being a strong advocate for Open Science, I am maintaining a Python package for reproducible and transparent mathematical modelling (https://essm.readthedocs.io) and contributing to the development of an open science platform (https://renkulab.io), which I am also using for my research. I am also editor of the open access and open-review journal Hydrology and Earth System Science (HESS).

Skill & Expertise

  •     Experimental plant ecophysiology
  •     Root uptake, plant hydraulics and stomatal control
  •     Plant and vegetation modelling
  •     Python programming
  •     Open science

 

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