Bayesian calibration of a growth‐dependent tree mortality model to simulate the dynamics of European temperate forests

• DOI: 10.1002/eap.2021
• Publication Date: 2019-10-12T13:44:52Z
• Authors: Maxime Cailleret; Nicolas Bircher; Florian Hartig; Lisa Hülsmann; Harald Bugmann
• Keywords: 580; 0106 biological sciences; 570; Bayesian calibration; growth; dynamic vegetation model; Bayes Theorem; Forests; 15. Life on land; Bayesian calibration; data integration; dynamic vegetation model; forest inventory; growth; stand dynamics; tree mortality; stand dynamics; 01 natural sciences; Europe; Calibration; tree mortality; tree mortality.; forest inventory; [SDE.BE]Environmental Sciences/Biodiversity and Ecology; data integration; Ecosystem

AbstractDynamic vegetation models (DVMs) are important tools to understand and predict the functioning and dynamics of terrestrial ecosystems under changing environmental conditions. In these models, uncertainty in the description of demographic processes, in particular tree mortality, is a persistent problem. Current mortality formulations lack realism and are insufficiently constrained by empirical evidence. It has been suggested that empirically estimated mortality submodels would enhanceDVMperformance, but due to the many processes and interactions within aDVM, the claim has rarely been tested. Here, we compare the performance of three alternative growth‐dependent tree mortality submodels in theDVMForClim: (1) a mortality function with theoretical foundation (ForClim v3.3); (2) a mortality function with parameters directly estimated based on forest inventory data; and (3) the same function, but with parameters estimated using an inverse approach through Bayesian calibration (BC). Time series of inventory data from 30 ecologically distinct Swiss natural forest reserves collected over 35+ yr, including the main tree species of Central Europe, were used for the calibration and subsequent validation of the mortality functions and theDVM. The recalibration resulted in mortality parameters that differed from the direct empirical estimates, particularly for the relationship between tree size and mortality. The calibrated parameters outperformed the direct estimates, and to a lesser extent the original mortality function, for predicting decadal‐scale forest dynamics at both calibration and validation sites. The same pattern was observed regarding the plausibility of their long‐term projections under contrasting environmental conditions. Our results demonstrate that inverse calibration may be useful even when direct empirical estimates ofDVMparameters are available, as structural model deficiencies or data problems can result in discrepancies between direct and inverse estimates. Thus, we interpret the good performance of the inversely calibrated model for long‐term projections (which were not a calibration target) as evidence that the calibration did not compensate for model errors. Rather, we surmise that the discrepancy was mainly caused by a lack of representativeness of the mortality data. Our results underline the potential for learning more about elusive processes, such as tree mortality or recruitment, through data integration inDVMs.