Using a forest patch model to predict the dynamics of stand structure in Swiss mountain forests

Wehrli, André; Zingg, Andreas; Bugmann, Harald; Huth, Andreas

doi:10.1016/j.foreco.2004.10.043

Cited by 36 publications

(30 citation statements)

References 30 publications

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“…Wehrli et al 2005). The results of these test runs showed that the mortality rates obtained with the dynamic crown structure were significantly lower than the rates obtained with ForClim V2.9.3, and very similar to the empirical mortality rates at the Sigriswil site (Table 3).…”

Section: Improvement Of the Crown Implementation In Forclimsupporting

confidence: 66%

“…1b) with its maximum around 44-52 cm DBH, the simulated mortality rates are comparable with values observed in other mountain forests of the region. For the three sites reported in Wehrli et al (2005), the empirical natural mortality rates for trees > 8 cm DBH during a period of 20 years were between 10 and 12%, with peaks up to 21%.…”

Section: Evaluation Of Stand Dynamicsmentioning

confidence: 97%

“…A detailed description of ForClim can be found in Bugmann (1996), and the latest version of ForClim, V.2.9.3, is documented in Risch et al (2005). Even though ForClim was not originally designed to simulate structural forest patterns such as diameter distributions, it has since been shown to accurately reproduce such patterns in simulations over periods of several decades (Risch et al 2005;Wehrli et al 2005).…”

Section: The Forclim Modelmentioning

confidence: 99%

“…However, Wehrli et al (2005) reported that the stressinduced mortality implemented in ForClim strongly overestimated mature tree mortality rates in dense stands, leading to an unrealistically drastic reduction of simulated tree numbers in short-term simulations. This artefactual effect arose from an underestimation of the calculated light availability on a patch, which in the current model is a function of the leaf area index (LAI) of idealized trees, i.e.…”

Section: The Forclim Modelmentioning

confidence: 99%

“…Huth and Ditzer 2000;Lindner et al 1997;Shugart 1998;Risch et al 2005;Wehrli et al 2005). The regeneration process, however, is normally simulated without great detail (cf.…”

Section: Introductionmentioning

confidence: 99%

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Improving the establishment submodel of a forest patch model to assess the long-term protective effect of mountain forests

et al. 2006

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Simulation models such as forest patch models can be used to forecast the development of forest structural attributes over time. However, predictions of such models with respect to the impact of forest dynamics on the long-term protective effect of mountain forests may be of limited accuracy where tree regeneration is simulated with little detail. For this reason, we improved the establishment submodel of the ForClim forest patch model by implementing a more detailed representation of tree regeneration. Our refined submodel included canopy shading and ungulate browsing, two important constraints to sapling growth in mountain forests. To compare the old and the new establishment submodel of ForClim, we simulated the successional dynamics of the Stotzigwald protection forest in the Swiss Alps over a 60-year period. This forest provides protection for an important traffic route, but currently contains an alarmingly low density of tree regeneration. The comparison yielded a significantly longer regeneration period for the new model version, bringing the simulations into closer agreement with the known slow stand dynamics of mountain forests. In addition, the new model version was applied to forecast the future ability of the Stotzigwald forest to buffer the valley below from rockfall disturbance. Two scenarios were simulated: (1) canopy shading but no browsing impact, and (2) canopy shading and high browsing impact. The simulated stand structures were then compared to stand structure targets for rockfall protection, in order to assess their long-term protective effects. Under both scenarios, the initial sparse level of tree regeneration affected the long-term protective effect of the forest, which considerably declined during the first 40 years. In the complete absence of browsing, the density of small trees increased slightly after 60 years, raising hope for an eventual recovery of the protective effect. In the scenario that included browsing, however, the density of small trees remained at very low levels. With our improved establishment submodel, we provide an enhanced tool for studying the impacts of structural dynamics on the long-term protective effect of mountain forests. For certain purposes, it is important that predictive models of forest dynamics adequately represent critical processes for tree regeneration, such as sapling responses to low light levels and high browsing pressure.

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Section: Improvement Of the Crown Implementation In Forclimsupporting

confidence: 66%

Section: Evaluation Of Stand Dynamicsmentioning

confidence: 97%

Section: The Forclim Modelmentioning

confidence: 99%

Section: The Forclim Modelmentioning

confidence: 99%

“…Huth and Ditzer 2000;Lindner et al 1997;Shugart 1998;Risch et al 2005;Wehrli et al 2005). The regeneration process, however, is normally simulated without great detail (cf.…”

Section: Introductionmentioning

confidence: 99%

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Improving the establishment submodel of a forest patch model to assess the long-term protective effect of mountain forests

et al. 2006

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How to kill a tree: empirical mortality models for 18 species and their performance in a dynamic forest model

Hülsmann

Bugmann

Cailleret

et al. 2018

Ecological Applications

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Dynamic Vegetation Models (DVMs) are designed to be suitable for simulating forest succession and species range dynamics under current and future conditions based on mathematical representations of the three key processes regeneration, growth, and mortality. However, mortality formulations in DVMs are typically coarse and often lack an empirical basis, which increases the uncertainty of projections of future forest dynamics and hinders their use for developing adaptation strategies to climate change. Thus, sound tree mortality models are highly needed. We developed parsimonious, species-specific mortality models for 18 European tree species using >90,000 records from inventories in Swiss and German strict forest reserves along a considerable environmental gradient. We comprehensively evaluated model performance and incorporated the new mortality functions in the dynamic forest model ForClim. Tree mortality was successfully predicted by tree size and growth. Only a few species required additional covariates in their final model to consider aspects of stand structure or climate. The relationships between mortality and its predictors reflect the indirect influences of resource availability and tree vitality, which are further shaped by species-specific attributes such as maximum longevity and shade tolerance. Considering that the behavior of the models was biologically meaningful, and that their performance was reasonably high and not impacted by changes in the sampling design, we suggest that the mortality algorithms developed here are suitable for implementation and evaluation in DVMs. In the DVM ForClim, the new mortality functions resulted in simulations of stand basal area and species composition that were generally close to historical observations. However, ForClim performance was poorer than when using the original, coarse mortality formulation. The difficulties of simulating stand structure and species composition, which were most evident for Fagus sylvatica L. and in long-term simulations, resulted from feedbacks between simulated growth and mortality as well as from extrapolation to very small and very large trees. Growth and mortality processes and their species-specific differences should thus be revisited jointly, with a particular focus on small and very large trees in relation to their shade tolerance.

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

Cailleret

Bircher²,

Härtig

et al. 2019

Ecological Applications

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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.

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Using a forest patch model to predict the dynamics of stand structure in Swiss mountain forests

Cited by 36 publications

References 30 publications

Improving the establishment submodel of a forest patch model to assess the long-term protective effect of mountain forests

Improving the establishment submodel of a forest patch model to assess the long-term protective effect of mountain forests

How to kill a tree: empirical mortality models for 18 species and their performance in a dynamic forest model

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

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