Models are pivotal for assessing future forest dynamics under the impacts of changing climate and management practices, incorporating representations of tree growth, mortality, and regeneration. Quantitative studies on the importance of mortality submodels are scarce. We evaluated 15 dynamic vegetation models (DVMs) regarding their sensitivity to different formulations of tree mortality under different degrees of climate change. The set of models comprised eight DVMs at the stand scale, three at the landscape scale, and four typically applied at the continental to global scale. Some incorporate empirically derived mortality models, and others are based on experimental data, whereas still others are based on theoretical reasoning. Each DVM was run with at least two alternative mortality submodels. Model behavior was evaluated against empirical time series data, and then, the models were subjected to different scenarios of climate change. Most DVMs matched empirical data quite well, irrespective of the mortality submodel that was used. However, mortality submodels that performed in a very similar manner against past data often led to sharply different trajectories of forest dynamics under future climate change. Most DVMs featured high sensitivity to the mortality submodel, with deviations of basal area and stem numbers on the order of 10-40% per century under current climate and 20-170% under climate change. The sensitivity of a given DVM to scenarios of climate change, however, was typically lower by a factor of two to three. We conclude that (1) mortality is one of the most uncertain processes when it comes to assessing forest response to climate change, and (2) more data and a better process understanding of tree mortality are needed to improve the robustness of simulated future forest dynamics. Our study highlights that comparing several alternative mortality formulations in DVMs provides valuable insights into the effects of process uncertainties on simulated future forest dynamics.
1.Swiss Fed Inst Technol, Forest Ecol, Univ Str 22, CH-8092 Zurich, Switzerland 2.Univ Nat Resources & Life Sci BOKU Vienna, Peter Jordan Str 82, A-1190 Vienna, Austria 3.Univ Regensburg, Theoret Ecol, Univ Str 31, D-93053 Regensburg, Germany 4.UFZ Helmholtz Ctr Environm Res, Leipzig, Germany 5.Karlsruhe Inst Technol, Inst Meteorol & Climate Res Atmospher Environm Re, Garmisch Partenkirchen, Germany 6.Czech Acad Sci, Inst Bot, Pruhonice, Czech Republic 7.Swiss Fed Inst Forest Snow & Landscape Res WSL, Res Unit Forest Dynam, Zurcherstr 111, CH-8903 Birmensdorf, Switzerland 8.Univ Liege, UR SPHERES, Unit Modelling Climate & Biogeochem Cycles, Liege, Belgium 9.Potsdam Inst Climate Impact Res PIK, Leibniz Assoc, Potsdam, Germany 10.Senckenberg Biodivers & Climate Res Ctr BiK F, Frankfurt, Germany 11.Goethe Univ, Dept Phys Geog, Frankfurt, Germany 12.Univ Osnabruck, Inst Environm Syst Res, Osnabruck, Germany 13.German Ctr Integrat Biodivers Res iDiv, Leipzig, Germany 14.Tech Univ Zvolen, Fac Forestry, TG Masaryka 24, Zvolen 96053, Slovakia 15.Bavarian State Inst Forestry LWF, Soil & Climate Dept, D-85354 Freising Weihenstephan, Germany 16.US Forest Serv, USDA, Northern Res Stn, Rhinelander, WI USA 17.Univ Barcelona, Dept Biol Evolut Ecol & Ciencies Ambientals, Av Diagonal 643, E-08028 Barcelona, Spain 18.Tech Univ Munich, TUM Sch Life Sci Weihenstephan, Freising Weihenstephan, Germany 19.Univ Grenoble Alpes, LESSEM, Irstea, F-38000 Grenoble, France 20.CREAF, Campus Bellaterra Edif C, Cerdanyola Del Valles 08193, Spain 21.Univ Milan, DISAA, I-20123 Milan, Italy 22.Los Alamos Natl Lab, Los Alamos, NM 87544 USA
Recommended Citation:
Bugmann, Harald,Seidl, Rupert,Hartig, Florian,et al. Tree mortality submodels drive simulated long-term forest dynamics: assessing 15 models from the stand to global scale[J]. ECOSPHERE,2019-01-01,10(2)