Tree mortality submodels drive simulated long‐term forest dynamics: assessing 15 models from the stand to global scale

• Published in: Ecosphere (Washington, D.C) Vol. 10; no. 2; pp. e02616 - n/a
• Main Authors: Bugmann, Harald, Seidl, Rupert, Hartig, Florian, Bohn, Friedrich, Brůna, Josef, Cailleret, Maxime, François, Louis, Heinke, Jens, Henrot, Alexandra‐Jane, Hickler, Thomas, Hülsmann, Lisa, Huth, Andreas, Jacquemin, Ingrid, Kollas, Chris, Lasch‐Born, Petra, Lexer, Manfred J., Merganič, Ján, Merganičová, Katarína, Mette, Tobias, Miranda, Brian R., Nadal‐Sala, Daniel, Rammer, Werner, Rammig, Anja, Reineking, Björn, Roedig, Edna, Sabaté, Santi, Steinkamp, Jörg, Suckow, Felicitas, Vacchiano, Giorgio, Wild, Jan, Xu, Chonggang, Reyer, Christopher P. O.
• Format: Journal Article Web Resource
• Language: English
• Published: United States John Wiley & Sons, Inc 01.02.2019; Ecological Society of America; Wiley-Blackwell; Wiley; John Wiley and Sons Inc
• Subjects: Algorithms; Biodiversity; Carbon; Climate change; climate change impacts; Drought; Earth sciences & physical geography; Environmental assessment; Environmental Sciences; Environmental Sciences & Ecology; forest dynamics; Forest ecosystems; Forests; Landscape; Life Sciences; model comparison; Mortality; mortality modeling; Physical, chemical, mathematical & earth Sciences; Physique, chimie, mathématiques & sciences de la terre; R&D; Research & development; Scale models; Sciences de la terre & géographie physique; Sensitivity; succession; succession; climate change impacts; mortality modeling; forest dynamics; model comparison
• ISSN: 2150-8925; 2150-8925
• DOI: 10.1002/ecs2.2616

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.