How robust are future projections of forest landscape dynamics? Insights from a systematic comparison of four forest landscape models

Gunnar Petter, Paola Mairota, Katharina Albrich, Peter Bebi, Josef Brůna, Harald Bugmann, Austin Haffenden, Robert M. Scheller, Dirk R. Schmatz, Rupert Seidl, Matthias Speich, Giorgio Vacchiano, Heike Lischke

Research output: Contribution to journalArticlepeer-review

41 Scopus citations

Abstract

Projections of landscape dynamics are uncertain, partly due to uncertainties in model formulations. However, quantitative comparative analyses of forest landscape models are lacking. We conducted a systematic comparison of all forest landscape models currently applied in temperate European forests (LandClim, TreeMig, LANDIS-II, iLand). We examined the uncertainty of model projections under several future climate, disturbance, and dispersal scenarios, and quantified uncertainties by variance partitioning. While projections under past climate conditions were in good agreement with observations, uncertainty under future climate conditions was high, with between-model biomass differences of up to 200 t ha−1. Disturbances strongly influenced landscape dynamics and contributed substantially to uncertainty in model projections (~25–40% of observed variance). Overall, model differences were the main source of uncertainty, explaining at least 50% of observed variance. We advocate a more rigorous and systematic model evaluation and calibration, and a broader use of ensemble projections to quantify uncertainties in future landscape dynamics.

Original languageEnglish
Article number104844
JournalEnvironmental Modelling and Software
Volume134
DOIs
StatePublished - Dec 2020

Keywords

  • Dispersal
  • Disturbances
  • Forest landscape models
  • Future projections
  • Model comparison
  • Variance partitioning

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