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

Abstract

Dynamic 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 enhance DVM performance, but due to the many processes and interactions within a DVM, the claim has rarely been tested. Here, we compare the performance of three alternative growth‐dependent tree mortality submodels in the DVM ForClim: (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 the DVM. 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 of DVM parameters 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 in DVMs.

Assessing the response of forest productivity to climate extremes in Switzerland using model–data fusion

“…So far, only few studies have assimilated extensive forest monitoring datasets into a DVM through techniques of parameter estimation (but see Cailleret et al, ; Fer et al, ; Minunno, Peltoniemi, et al, ; Thomas et al, ), even though recommended by several authors to improve large‐scale model projections (Dietze et al, ; Hartig et al, ). Our study demonstrates that it is possible to integrate monitoring data from multiple networks across a wide bioclimatic gradient into a process‐based forest ecosystem model 3‐PG.…”

“…Despite the fact that this method allows us to combine multiple data sources and types, most studies have focused on the local scale. Hence, an important step forward is now to use large and diverse datasets in combination with DVMs at the regional scale (Cailleret, Bircher, Hartig, Hülsmann, & Bugmann, 2019;Fer et al, 2018;Minunno, Peltoniemi, et al, 2019;Thomas et al, 2017;Van Oijen et al, 2013).…”

Assessing the response of forest productivity to climate extremes in Switzerland using model-data fusion

“…So far, only few studies have assimilated extensive forest monitoring datasets into a DVM through techniques of parameter estimation (but see Cailleret et al, 2019;Fer et al, 2018;Minunno, Peltoniemi, et al, 2019;Thomas et al, 2017), even though recommended by several authors to improve large-scale model projections (Dietze et al, 2014;Hartig et al, 2012). Our study demonstrates that it is possible to integrate monitoring data from multiple networks across a wide bioclimatic gradient into a process-based forest ecosystem model 3-PG.…”

Climate and parameter sensitivity and induced uncertainties in carbon stock projections for European forests (using LPJ-GUESS 4.0)

“…Thereby, it should be investigated if these uncertainties stem from the intra-specific variability of the parameters itself (Bolnick et al, 2011) or the parameters not being identifiable (see Marsili-Libelli et al, 2014) or if a model-data comparison could reduce uncertainties in the parameters (e.g., Hartig et al, 2011;Dietze, 2017b). Using time series inventory data might help as they are informative for constraining mortality modules (Cailleret et al, 2020). Second, lower sensitivities of establishmentrelated parameters are surprising as we know that not all three investigated species can effortlessly establish across all of Europe, e.g., Fag.…”