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.
A recent remote sensing study, published in Nature Communications, reveals consistently increasing trends in forest canopy mortality over Central Europe.
In a recent study, published in Nature Communications, we found that global disturbance patterns in the temperate biome are highly variable, and shaped by variation in disturbance agents and traits of prevailing tree species.
PhD student Katharina Albrich visited Prof. Monica G. Turner and her team at the University of Wisconsin in Madison!
Legacy effects from past disturbances and climatic extremes drive recent disturbances in the primary forests of Central Europe, finds a study recently published in Global Change Biology.
A recent study published in Ecological Applications found a trade-off between temporal stability and level of provisioning for three forest ecosystem services.
The Austrian public television ORF invited Rupert Seidl to talk about the latest RESIN publication on “Aktuell in Österreich”
On March 22-23, 2018 we held a workshop focused on modeling natural disturbances in the Western Carpathian mountains
Harnessing landscape heterogeneity can help address changing disturbance regimes in management, finds a recent study published in the Journal of Environmental Management.
A recent study published in Global Change Biology for the first time documented continental-scale synchronization of forest disturbances in Europe.