Accuracy, realism and general applicability of European forest models

Forest models are instrumental for understanding and projecting the impact of climate change on forests. A considerable number of forest models have been developed in the last decades. However, few systematic and comprehensive model comparisons have been performed in Europe that combine an evaluation of modelled carbon and water fluxes and forest structure. We evaluate 13 widely used, state-of-the-art, stand-scale forest models against field measurements of forest structure and eddy-covariance data of carbon and water fluxes over multiple decades across an environmental gradient at nine typical European forest stands. We test the models\u27 performance in three dimensions: accuracy of local predictions (agreement of modelled and observed annual data), realism of environmental responses (agreement of modelled and observed responses of daily gross primary productivity to temperature, radiation and vapour pressure deficit) and general applicability (proportion of European tree species covered). We find that multiple models are available that excel according to our three dimensions of model performance. For the accuracy of local predictions, variables related to forest structure have lower random and systematic errors than annual carbon and water flux variables. Moreover, the multi-model ensemble mean provided overall more realistic daily productivity responses to environmental drivers across all sites than any single individual model. The general applicability of the models is high, as almost all models are currently able to cover Europe\u27s common tree species. We show that forest models complement each other in their response to environmental drivers and that there are several cases in which individual models outperform the model ensemble. Our framework provides a first step to capturing essential differences between forest models that go beyond the most commonly used accuracy of predictions. Overall, this study provides a point of reference for future model work aimed at predicting climate impacts and supporting climate mitigation and adaptation measures in forests

Accuracy, realism and general applicability of European forest models

Forest models are instrumental for understanding and projecting the impact of climate change on forests. A considerable number of forest models have been developed in the last decades. However, few systematic and comprehensive model comparisons have been performed in Europe that combine an evaluation of modelled carbon and water fluxes and forest structure. We evaluate 13 widely used, state-of-the-art, stand-scale forest models against field measurements of forest structure and eddy-covariance data of carbon and water fluxes over multiple decades across an environmental gradient at nine typical European forest stands. We test the models' performance in three dimensions: accuracy of local predictions (agreement of modelled and observed annual data), realism of environmental responses (agreement of modelled and observed responses of daily gross primary productivity to temperature, radiation and vapour pressure deficit) and general applicability (proportion of European tree species covered). We find that multiple models are available that excel according to our three dimensions of model performance. For the accuracy of local predictions, variables related to forest structure have lower random and systematic errors than annual carbon and water flux variables. Moreover, the multi-model ensemble mean provided overall more realistic daily productivity responses to environmental drivers across all sites than any single individual model. The general applicability of the models is high, as almost all models are currently able to cover Europe's common tree species. We show that forest models complement each other in their response to environmental drivers and that there are several cases in which individual models outperform the model ensemble. Our framework provides a first step to capturing essential differences between forest models that go beyond the most commonly used accuracy of predictions. Overall, this study provides a point of reference for future model work aimed at predicting climate impacts and supporting climate mitigation and adaptation measures in forests.Peer reviewe

Mobilise-D insights to estimate real-world walking speed in multiple conditions with a wearable device

This study aimed to validate a wearable device’s walking speed estimation pipeline, considering complexity, speed, and walking bout duration. The goal was to provide recommendations on the use of wearable devices for real-world mobility analysis. Participants with Parkinson’s Disease, Multiple Sclerosis, Proximal Femoral Fracture, Chronic Obstructive Pulmonary Disease, Congestive Heart Failure, and healthy older adults (n = 97) were monitored in the laboratory and the real-world (2.5 h), using a lower back wearable device. Two walking speed estimation pipelines were validated across 4408/1298 (2.5 h/laboratory) detected walking bouts, compared to 4620/1365 bouts detected by a multi-sensor reference system. In the laboratory, the mean absolute error (MAE) and mean relative error (MRE) for walking speed estimation ranged from 0.06 to 0.12 m/s and − 2.1 to 14.4%, with ICCs (Intraclass correlation coefficients) between good (0.79) and excellent (0.91). Real-world MAE ranged from 0.09 to 0.13, MARE from 1.3 to 22.7%, with ICCs indicating moderate (0.57) to good (0.88) agreement. Lower errors were observed for cohorts without major gait impairments, less complex tasks, and longer walking bouts. The analytical pipelines demonstrated moderate to good accuracy in estimating walking speed. Accuracy depended on confounding factors, emphasizing the need for robust technical validation before clinical application. Trial registration: ISRCTN – 12246987

Towards robust projections of future forest dynamics: why there is no silver bullet to cope with complexity

Forest ecosystems are of key importance for providing a broad range of ecosystem goods and services. Yet, forest ecosystems are subject to manifold pressures and considered to be particularly sensitive to rapid climate change. A particular uncertainty remains in larger scale assessments (i.e., regional to national scale) due to the effects of local-scale drivers of climate change impacts (e.g., related to site-, species- and stand-specific responses), which is particularly important for forest management and planning. Multiple dynamic vegetation models (DVMs) have been developed to analyze forest ecosystems and their dynamics. Among the various types of DVMs, forest gap models (FGMs) have been introduced to represent stand-scale dynamics as upscaled from individual tree behavior, thus allowing to represent uneven-aged, mixed-species stands. This renders them principally suitable to assess the future development of today’s forest stands under changing environmental conditions while accounting for site-specific factors. Even though many ecological models strive for generality and realism, so far few FGMs have been applied across large areas (e.g., the national to continental scale). This may be due to parameterization problems and insufficient representation of underlying mechanisms. To overcome these issues, many models are (re-)calibrated locally or adapted structurally to reflect local conditions. Yet, such strategies tend not to increase a model’s general applicability across a broad range of conditions. Furthermore, model developments aiming at structural improvements typically focus on one single process. However, due to unaccounted process interactions, such efforts are at risk of ‘getting the right patterns for the wrong reason’ (i.e., missing ecological reality). Thus, recent pleas have been made to jointly revisit the ecological processes at the core of DVMs to increase the consistency of process interactions. Using the state-of-the-art FGM ForClim, this PhD thesis aims to enhance the robustness of the projections for various fields of FGM applications (e.g., natural and managed forest stand dynamics) across a range of spatial and temporal scales. A particular focus was on the consistency of the simulated processes and their interactions. Specifically, these efforts were geared towards providing locally accurate climate impact assessments over large areas, representing a current research frontier in FGMs and DVMs in general. In Chapter 1, I conducted a sensitivity analysis of the ForClim model to analyze model behavior and identify model components that deserve particular attention for reducing uncertainties of parameter estimates and/or improving process representation. Since the relative parameter influence may not be constant over time and likely varies with site conditions, stand structure and species composition, I analyzed the model’s parameter sensitivity at 30 representative sites across Europe and compared results for monospecific and mixed stands at two system states in time (i.e., early and late succession). Key parameters causing the largest variability in model outputs were related to tree establishment, the water and light regimes, growth and temperature, whereby the relative parameter influence of the latter strongly varied with local climate conditions. Moreover, model sensitivity differed between monospecific and mixed stands as well as between early and late successional stages, reflecting the differential influence of ecological processes with stand structure. In addition, model application at a European scale (i.e., far beyond the geographical range ForClim was originally developed for) pointed at model shortcomings. Since ForClim proved to be highly sensitive to water-related parameters across most of the European continent (Chapter 1) and accurately representing water limitations in DVMs is of utmost importance in view of climate change, I revisited ForClim’s water module in Chapter 2. Most DVMs explicitly model water availability based on a water balance with potential evapotranspiration (PET) as the main driver. I assessed the performance of ForClim’s water module, which includes the PET formulation by Thornthwaite and Mather (1957), by confronting simulated with observed monthly AET at forested FLUXNET sites. Further, I included alternative PET formulations in the comparison, particularly the one by Priestley and Taylor (1972) featuring a higher degree of mechanistic detail. The performance of the water module as applied in ForClim depended primarily on climate type independent of the PET formulation applied. I thus conclude that increasing the complexity of the PET formulation will hardly improve the estimates of water deficiencies at an annual scale in ForClim. Rather, more attention should be payed to forest-specific features in the context of the water balance, such as the representation of belowground and phenological processes, because most water balance models have been developed for agricultural applications. In Chapter 3, I scrutinized the representation of ForClim’s core processes and the consistency of their interplay. I developed a set of alternative process and parameter representations for the core processes light availability, tree establishment, growth and mortality, based on ecological theory and diverse sources of empirical data. I applied a pattern-oriented modeling (POM) approach to test all combinations of the standard and alternative formulations (yielding 504 model versions) against a comprehensive set of empirical patterns for diverse model applications and a wide range of site conditions. I found that adapting one process in isolation can improve model performance for one specific application. However, the best model versions typically included more than one alternative process or parameter formulation. Thus, simultaneously considering multiple core processes is key for revealing internal inconsistencies in the model framework and model improvements. In this context, POM proved to be highly suitable to bridge various fields of model application and to compare model outputs with a broad set of patterns comprising diverse forest characteristics at different temporal and spatial scales. I conclude that the forest ecology community should make good use of the ever-increasing data availability and the POM framework to challenge the core processes of DVMs in a holistic manner. Finally, the increasing impacts of climate change on forest ecosystems have triggered multiple model-based impact assessments for the future, which however feature considerable uncertainty regarding local impacts over larger areas (i.e., regional to national scales). In Chapter 4, I aimed at bridging this gap by analyzing the climate change sensitivity until the end of the 22nd century for 71 typical managed Swiss forests. To account for various sources of uncertainty in the projections, the effect of eight different model versions (developed in Chapter 3) as well as alternative soil types and climate change scenarios were considered. The simulations showed substantial changes in basal area and species composition, with dissimilar responses to climate change across and within elevation zones. I identified the following stands as being most prone to negative climate-induced impacts: (1) stands in the sub-montane and low montane elevations zones and (2) stands located on poor soils in the high montane and subalpine elevation zones. The introduction of additional, more climatically adapted species partly mitigated the negative impacts of climate change, suggesting that admixing drought-tolerant species is advisable across all elevations to increase the resistance and resilience of forest stands to climate change. Yet, the large influence of site conditions and the choice of the forest model on some of the projections indicates that uncertainty sources other than the climate change scenarios need to be considered in impact assessments. By considering diverse sources of uncertainty, including structural and parameter-related uncertainties of the model, I was thus able to demonstrate their key relevance for an improved, evidence-based decision support in forest management under climate change. Throughout the thesis, I presented an approach to improve the robustness, accuracy and generality of FGMs, and demonstrated how to upscale climate-change impact assessments from the local to the national scale, which I believe are important steps in advancing the frontier of large-scale applications of FGMs. The insights from this thesis are furthermore relevant for other DVMs, especially for those that feature strong structural similarity with ForClim

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

Dynamic forest models are a key tool to better understand, assess, and project decadal‐ to centennial‐scale forest dynamics. Despite their success, many questions regarding appropriate model formulations remain unresolved, and few models have found widespread application, for example, across a whole continent. We aimed to scrutinize the representation of ecological processes in dynamic forest models so as to rigorously test core assumptions underlying forest dynamics and the consistency of their interplay, taking the ForClim model as a case study. We developed a set of alternative representations for the main ecological processes, that is, tree establishment, growth, and mortality, and light extinction through the canopy, based on diverse sources of empirical data. We applied a pattern‐oriented modeling (POM) approach to test all combinations of the standard and alternative formulations (>500 model versions) against a comprehensive set of patterns for diverse model applications across a wide range of site conditions. We found that adapting one process in isolation can improve model performance for one specific application. However, the best model versions typically included more than one alternative formulation. Importantly, the best version for an individual application was generally not the best across multiple applications, emphasizing the varying influences of ecological processes. We conclude that the behavior and performance of complex models should not be analyzed for a few specific applications only. Rather, multiple applications, system states, and dynamics of interest should be scrutinized across a wide range of site conditions. This allows for avoiding overfitting and detecting and eliminating structural shortcomings and parameterization problems. We thus propose to make use of the ever‐increasing data availability and the POM framework to challenge the core processes of dynamic models in a holistic manner. For model applications, we propose that a set of alternative formulations (ensemble simulations) should be used to quantify the impacts of structural uncertainty, rather than to rely on the projections from one single model version

The Habitat Map of Switzerland: A Remote Sensing, Composite Approach for a High Spatial and Thematic Resolution Product

Habitat maps at high thematic and spatial resolution and broad extents are fundamental tools for biodiversity conservation, the planning of ecological networks and the management of ecosystem services. To derive a habitat map for Switzerland, we used a composite methodology bringing together the best available spatial data and distribution models. The approach relies on the segmentation and classification of high spatial resolution (1 m) aerial imagery. Land cover data, as well as habitat and species distribution models built on Earth observation data from Sentinel 1 and 2, Landsat, Planetscope and LiDAR, inform the rule-based classification to habitats defined by the hierarchical Swiss Habitat Typology (TypoCH). A total of 84 habitats in 32 groups and 9 overarching classes are mapped in a spatially explicit manner across Switzerland. Validation and plausibility analysis with four independent datasets show that the mapping is broadly plausible, with good accuracy for most habitats, although with lower performance for fine-scale and linear habitats, habitats with restricted geographical distributions and those predominantly characterised by understorey species, especially forest habitats. The resulting map is a vector dataset available for interactive viewing and download from open EnviDat data sharing platform. The methodology is semi-automated to allow for updates over time

Stand‐scale climate change impacts on forests over large areas: transient responses and projection uncertainties

The increasing impacts of climate change on forest ecosystems have triggered multiple model‐based impact assessments for the future, which typically focused either on a small number of stand‐scale case studies or on large scale analyses (i.e., continental to global). Therefore, substantial uncertainty remains regarding the local impacts over large areas (i.e., regions to countries), which is particularly problematic for forest management. We provide a comprehensive, high‐resolution assessment of the climate change sensitivity of managed Swiss forests (ca. 10’000 km2), which cover a wide range of environmental conditions. We used a dynamic vegetation model to project the development of typical forest stands derived from a stratification of the 3rd National Forest Inventory until the end of the 22nd century. Two types of simulations were conducted: one limited to using the extant local species, the other enabling immigration of potentially more climate‐adapted species. Moreover, to assess the robustness of our projections, we quantified and decomposed the uncertainty in model projections resulting from the following sources: (i) climate change scenarios, (ii) local site conditions and (iii) the dynamic vegetation model itself (i.e., represented by a set of model versions), an aspect hitherto rarely taken into account. The simulations showed substantial changes in basal area and species composition, with dissimilar sensitivity to climate change across and within elevation zones. Higher‐elevation stands generally profited from increased temperature, but soil conditions strongly modulated this response. Low‐elevation stands were increasingly subject to drought, with strong negative impacts on forest growth. Furthermore, current stand structure had a strong effect on the simulated response. The admixture of drought‐tolerant species was found advisable across all elevations to mitigate future adverse climate‐induced effects. The largest uncertainty in model projections was associated with climate change scenarios. Uncertainty induced by the model version was generally largest where overall simulated climate change impacts were small, thus corroborating the utility of the model for making projections into the future. Yet, the large influence of both site conditions and the model version on some of the projections indicates that uncertainty sources other than climate change scenarios need to be considered in climate change impact assessments.ISSN:1051-0761ISSN:1939-558

Countrywide classification of permanent grassland habitats at high spatial resolution

Abstract European grasslands face strong declines in extent and quality. Many grassland types are priority habitats for national and European conservation strategies. Countrywide, high spatial resolution maps of their distribution are often lacking. Here, we modelled the spatial distribution of 20 permanent grassland habitats at the level of phytosociological alliances across Switzerland at 10x10 m resolution. First, we applied ensemble models to provide distribution maps of the individual habitat types, using training data from various sources. Copernicus Sentinel satellite imagery and variables describing climate, soil and topography were used as predictors. The performance of these models was assessed based on the true skill statistics with a split‐sampling of the data. Second, the individual maps were combined into countrywide maps of the most and second most likely habitat type, respectively, using an expert‐based weighting approach. The performance of the combined map for the most likely habitat type was assessed via an independent testing dataset and a comparison of the predicted habitat‐type proportions with extrapolations from field surveys. Most individual maps had useful to excellent predictive performance (TSS ≥ 0.6). For most grid cells in the combined maps, the most and second most likely habitat types were either ecologically closely related or representing two grassland types along a nutrient gradient. The same was true for omission errors. We found good agreement between the predicted and estimated proportions from field surveys. The area of raised bogs appears to be underestimated, while dry grasslands showed highest agreement. This work highlights the potential of earth observation data at fine spatial and temporal resolution to map habitats at broad scales, thereby providing the foundation for diverse conservation applications. A particular challenge remains in capturing the transition from nutrient‐poor to nutrient‐rich grasslands, which is highly important for biodiversity conservation