BayForDemo

Strategies for adapting Bavarian forests to climate change based on the simulation of demographic processes

Forests are an integral component of the Bavarian landscape and merit special protection and appreciation because of their diverse ecosystem goods and services. As trees are long-living organisms, forests adapt to environmental changes much slower that more dynamic ecosystems. Hence, global climate change has a high potential to threaten forest ecosystems. This calls for robust predictive tools to improve the quantification of the long-term development of productivity, carbon sequestration and species composition of Bavarian forests.

This is where the project BayForDemo comes in: For the projection of forest development, an empirically parameterized simulation environment is developed that describes tree growth, mortality and regeneration in response to climate and biotic interactions. For calibrating this demographic simulator, information from a large variety of forest data sources is combined using Bayesian methods. By this means, also processes that are only poorly informed by observations, e.g. regeneration, can be calibrated. This approach allows for an improved understanding of the demographic diversity of European tree species, the intraspecific variability and the effects of environmental drivers.

Cycle of calibrating and applying the demographic simulator using Bayesian methods.

These data-driven projections of forest development have a high potential to improve the identification of vulnerable forest stands, the selection of tree species and the quantification of productivities. Additionally, it is possible to identify particularly uncertain subprocesses, tree species or regions and to systematically expand their monitoring. New data gathered this way can be integrated in the existing framework using flexible options of recalibration to continuously strengthen the simulation environment. The project BayForDemo thus contributes to the adaptive management of forest resources in Bavaria to maintain forest functioning in the future.

Principal investigator
Prof. Dr. Lisa Hülsmann
Ecosystem Analysis & Simulation
Faculty of Biology, Chemistry and Geosciences
University of Bayreuth
Dr.-Hans-Frisch-Straße 1-3
95448 Bayreuth
Tel: +49 921 55 5650

 

Publikationen

  • Accounting for foliar gradients in VCmax and Jmax improves estimates of net CO2 exchange of forests
    Bachofen, C., L. Hülsmann, A. Revill, N. Buchmann, and P. D'Odorico
    Agricultural and Forest Meteorology 2022; 314
  • Climate-driven, but dynamic and complex? A reconciliation of competing hypotheses for species’ distributions
    Schultz EL, Hülsmann L, Pillet MD et al.
    Ecology Letters 2021
  • An evaluation of multi-species empirical tree mortality algorithms for dynamic vegetation modelling
    Thrippleton T, Hülsmann L, Cailleret M, Bugmann H
    Scientific Reports 11(1), 2021; 11(1): 19845
  • Data and analysis for: Divergent occurrences of juvenile and adult trees are explained by both environmental change and ontogenetic effects
    Heiland, L., G. Kunstler, P. Ruiz-Benito, A. Buras, J. Dahlgren, and L. Hülsmann
    Dryad 2021
  • How future-proof is Sweet chestnut (Castanea sativa) in a global change context?
    Conedera, M., P. Krebs, E. Gehring, J. Wunder, L. Hülsmann, M. Abegg, and J. Maringer
    Forest Ecology and Management 2021: 494
  • Is variation in conspecific negative density dependence driving tree diversity patterns at large scales?
    Hülsmann L, Chisholm R , Hartig F
    Trends in Ecology & Evolution 2020; 36(2): 151-163
  • Bayesian calibration of a growth‐dependent tree mortality model to simulate the dynamics of European temperate forests
    Cailleret M, Bircher N, Hartig F, Hülsmann L, Bugmann H
    Ecological Applications 2020; 30 (1)
  • Projecting forest dynamics across Europe: Potentials and pitfalls of empirical mortality algorithms
    Thrippleton T, Hülsmann L, Cailleret M, Bugmann H
    Ecosystems 2019; 23(1): 188-203
  • “Big Data” auch im Wald
    Hülsmann, L.
    Blick in die Wissenschaft 2019; 28(39): 56-57

Dissertationen

    Abschlussarbeiten

    • Mortality patterns of Castanea sativa in southern Switzerland
      Lucas Weiß
      Bachelorarbeit BayForDemo 2018