Résumé : |
(auteur) A high diversity of forest ecosystems is found around the globe providing various ecosystem services to humans. Responses of forests to recent increases of drought events have given rise to serious concerns about future forest development. Since anthropogenic climate change is proceeding at an unprecedented rate, the forestry sector is challenged to swiftly develop and plan adaptive management measures that guarantee the sustainable provision of forest ecosystem services in the future. The planning of management strategies is strongly dependent on reliable knowledge on future forest dynamics. To this end, the Swiss government has launched an extensive research program to examine the impact of climate change on Swiss forests. One aim among others is to assess the sensitivity of common forest types of Switzerland to climate change.
Dynamic vegetation models (DVMs) are suitable to provide quantitative assessments of forest sensitivity to climate change, as their flexibility allows considering dynamic vegetation transitions under conditions that do not represent a steady state. Among DVMs, forest gap models portray long-term forest dynamics at the stand scale taking biotic interactions such as competition into account. Recent integration of sophisticated management techniques has substantially extended their range of application from unmanaged to complex mixed-species forests under management, thus making them interesting tools for the assessment of climate change impacts on forest ecosystems. However, forest gap models integrate a large number of ecological processes that still lack an empirical base. This is particularly true for tree mortality – a key demographic process in forest dynamics – where increasing empirical research has been followed by little action in DVMs. Thus, although it is widely acknowledged that empirical functions should be integrated into DVMs to enhance ecological realism, little is known about whet her this approach leads to an increased robustness of model projections.
Given this background, my thesis includes two major objectives: 1) to examine the potential of empirical mortality functions in dynamic vegetation models and 2) to assess the sensitivity of common Swiss forests to climate change.
In Chapter 1 of this thesis, I implemented an inventory- and a tree-ring based mortality function in the forest gap model ForClim and combined them with a stochastic and a deterministic approach for the determination of tree status (alive vs. dead). These four new model versions were tested for two Norway spruces stands, one of which was managed (inventory time series of 72 years) and the other was unmanaged (41 years). Furthermore, I ran long-term simulations (~400 years) into the future to test model behavior under three climate scenarios. I showed that three out of the four mode l versions showed good agreement for stand basal area and stem numbers when compared against inventory data of both forest sites. Due to very similar model behavior, an unambiguous choice of a “best” model version was, however, not possible. In contrast, long -term simulations revealed very different behavior of the mortality models, indicating that the choice of the mortality function is crucial for simulated forest dynamics. Based on these results, I concluded that 1) empirical mortality functions are valuable replacements for current theoretical mortality algorithms in dynamic vegetation models 2) but further tests would be needed to rigorously assess their potential and to better understand interactions of the mortality function with other model processes.
Enhanced use of empirical data in dynamic vegetation models is widely advocated. However, it is largely unknown whether empirically derive d functions are compatible with the wide range of processes and interactions that are usually found in DVMs and thus, whether they lead to an better model performance. In Chapter 2 , I addressed this question with the focus on the inventory-based mortality function that has already been used in Chapter 1 . I used Bayesian methods to recalibrate its mortality parameters within ForClim. I compared its performance with the ForClim version containing the original, empirically fitted mortality parameters and with the current ForClim v3.3 that included a theoretical mortality function. Calibration and subsequent validation was based on inventory data of 30 Swiss natural forest reserves. Similarities between the calibrated and the empirically fitted mortality parameters suggest that the general structure of ForClim is appropriate to integrate empirical mortality functions. However, I found some discrepancies that indicate necessary improvements regarding the role of species’ shade tolerance in growth-mortality relationships and an optimal balance between growth and mortality. Bayesian calibration led to best performance both at calibration and validation sites. Furthermore, it revealed that the sensitivity of ForClim to parametric uncertainty is particularly high for trees in low dbh classes but surprisingly small for standard model outputs such as basal area.
Assessing the sensitivity of common forest stands in Switzerland with a forest gap model makes it necessary 1) to know which forest stands are common and 2) to have suitable data for model initialization. In Chapter 3 , I developed a stratification of the Swiss forest area to identify those forest types of Switzerland that , in terms of their stand structure and tree species composition, are most common in different eco-regions and elevation zones. I used plot data form the third Swiss National Forest Inventory (NFI3) that contained both stand attributes and single-tree data. NFI plots were grouped into eco -regions and elevation zones according to the “Guide for sustainability in protection forests” (NaiS). I further segregated NFI plots into more groups based on two forest stand attributes: vertical stand structure and developmental stage. In a last step, I relied on recommendations of sylvicultural experts for dividing some groups into more strata to strengthen a realistic tree species composition. The stratification resulted in 71 strata that contained 25% of all NFI forest plots. Single-tree data of all NFI plots associated to one stratum were aggregate d. Although the final result is a somewhat “artificial” forest stand, it has the tremendous advantage that NFI plot data can be used directly for stand initialization in the forest gap model ForClim.
In Switzerland, studies on forest sensitivity to climate change often focus on extreme sites where shifts in tree species composition are already visible while less attention is paid to the fate of common forest stands that are most important for Swiss forestry. In Chapter 4, I ran simulations for 71 strata that had been identified in the previous chapter using two model versions to examine their development until the end of the 21 st century (year 2100). Simulations were run with common Swiss forest management strategies and without management. I considered forest development under current climate (1980-2009) and under 11 different climate change scenarios assuming an A2 greenhouse gas emission scenario. According to these simulation results, shifts in structure and composition of Swiss forests have to be expected for the second half of this century. However, high variability among the strata was found due to drivers of small-scaled forest dynamics such as regional climate, elevation gradients and current species composition. I showed that current management regimes can alleviate the negative impacts of climate change but adaptive measures are necessary to be applied at a site-specific and objective-oriented base. In conclusion, model- based assessments on forest sensitivity can only provide reliable decision-making support for forest managers if small-scaled drivers of forest stand dynamics are take n into consideration.
In the Synthesis, I reflect the findings of the previous chapters by discussing the potential of empirical mortality functions in DVMs and the use of forest gap models – as one type of DVM – as tools for decision-support regarding forest management under climate change. I come to the conclusion that empirical mortality functions are capable to further improve the performance of DVMs and to increase our confidence in their projections. However, empirical functions come with limitations, which might constrain avalid applicability. For this reason, I advocate not to focus on one individual function but to aggregate knowledge on mortality mechanism and data from various sources to enhance the validity of the tree mortality mechanism in DVMs beyond individual empirical data sets. Climate change is expected to have strong effects on future development of current Swiss forests at various sites. High variability in forest response to a changing environment underlines the need to plan future forest strategies at the local scale. Forest gap models have limitations that need to be discussed and tackled. Still, I am convinced that they have the potential to play a key role in decision-making processes as they can provide what decision makers need: a comprehensive reflection of essential processes and an adequate spatial resolution. |