Forest disturbance in a changing world



A new approach for scaling vegetation dynamics

A novel approach for scaling vegetation dynamics based on deep learning was published recently in Methods in Ecology and Evolution. The modeling framework developed by Werner Rammer and Rupert Seidl allows a consistent scaling of local vegetation dynamics (with abundant data and high process understading) to much larger spatial extents (think: country to continental level). At the core, the model harnesses deep learning, which is an exciting new branch of machine learning that revolutionized many fields of computer science in the last years.

Written by Admin on Friday March 15, 2019


Sucessful research visit to the US

PhD student Katharina Albrich visited Prof. Monica G. Turner and her team at the University of Wisconsin in Madison!

Written by katharina on Monday November 5, 2018

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About the project

Natural disturbances such as windthrows, insect outbreaks, and wildfires have intensified in many forest ecosystems around the globe recently. The project “Forest disturbance in a changing world” (RESIN) addresses the causes, consequences, and responses of changing forest disturbance regimes.

Specifically, RESIN aims to

  • improve our understanding of the past, present, and future of the natural disturbance regimes of Central Europe’s forests,
  • assess the effects of disturbance changes on biological diversity and the ecosystem services forests provide for society, and
  • develop robust management approaches to sustain ecosystems and their services also under changing climate and disturbance regimes

RESIN was started in 2016 and is a 6-year project, bringing together an international team of scientists under the lead of Rupert Seidl from BOKU Vienna.

 RESIN is supported by a START grant of the Austrian Science Fund.