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

Abstract

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.

Hundred year projected carbon loads and species compositions for four National Forests in the northwestern USA

“…The dClim rule uses the change in local climate associated with a shift in elevation as a proxy for adaptiveness. This study demonstrated that varying the influence of the dClim rule can have a significant impact on future carbon and species composition estimates; similar results were reported in Diaz et al [37] and Bugmann et al [49]. Users of Climate-FVS must inspect results to ensure they are reasonable and consider the influence of parameterization, particularly with regard to the dClim rule.…”

“…It is not known to what degree, if any, and how fast this mortality will occur in real forests though existing research indicates that climate-induced mortality from drought, wildfire, and insect epidemics could be widespread [50,51]. Climateinduced mortality functions vary among climate sensitive vegetation models, and a recent study found Climate-FVS was noted to be more sensitive to climate-induced mortality than non-climate induced mortality (e.g., mortality due to increasing stand density) among the models tested [49]. Toward the end of the simulations, after the dClim rule affected tree mortality rates, carbon projections stabilized or began to increase.…”

“…A major focus of this study is the dClim rule used by Climate-FVS, which is a model component that has not, and cannot, be validated. Empirically-based models, such as FVS, were calibrated using historical data and the underlying assumptions associated with that calibration might not be valid under future climates [49]. Only further research on individual tree mortality along dynamic climatic gradients can support the assumptions underlying the dClim rule and assist in tuning this factor appropriately.…”

Capturing ecological processes in dynamic forest models: why there is no silver bullet to cope with complexity

“…Yet, the development of DVMs is challenging since many processes are involved and interact across temporal and spatial scales (Bugmann 2001, Keane et al 2001, Price et al 2001). Thus, large uncertainties remain regarding the quantification of the ecological processes that govern forest dynamics, including both structural and parameter‐related uncertainties (Fortin et al 2009, Hlásny et al 2014, Horemans et al 2016, Reyer et al 2016, Neumann et al 2017, Huber et al 2018, Bugmann et al 2019). Not surprisingly, a plethora of model formulations are used in DVMs, but they are rarely benchmarked rigorously.…”

“…Not surprisingly, a plethora of model formulations are used in DVMs, but they are rarely benchmarked rigorously. Yet, alternative process formulations have the potential to strongly alter simulation results, particularly when DVMs are applied for climate change impact assessments (Nishina et al 2015, Horemans et al 2016, Bugmann et al 2019).…”