Forest Gap Model Research Articles

Integrating remotely sensed imagery in a forest gap model to study North American boreal forests in a changing world

Abstract Boreal forests of Alaska and Western Canada are experiencing rapid climate change characterized by higher temperatures, more extreme droughts, and changing disturbance regimes, resulting in forest mortality and composition changes. Mechanistic models are increasingly important for predicting future forest trends as the region experiences novel environmental change. Previously, many process-based models have generated starting conditions by ‘spinning up’ to equilibrium. However, setting appropriate initial conditions remains a persistent challenge in using mechanistic forest models, where stochastic events and latent parameters governing tree establishment have long-lasting impacts on simulation outcomes. Recent advances in remote sensing analysis provide information that can help address this issue. We updated an individual-based gap model, the University of Virginia Forest Model Enhanced (UVAFME), to include initial conditions derived from aerial and satellite imagery at two locations. Following these updates, material legacies (e.g., trees, seed banks, soil organic layer) allowed new forest types to persist in UVAFME simulations, landscape-level forest heterogeneity increased, and forest-wide biomass estimates increased. At both study sites, initialization from remotely sensed data had a strong impact on forest cover and volume. Climate change impacts were simulated decades earlier than when the model was ‘spun up'. In Alaska’s Tanana Valley State Forest, warmer climate scenarios drove deciduous expansion, increased drought stress, and resulted in a 28% decrease in overall biomass by 2100 between historical and high emissions climate scenarios. At a lowland site in Northern British Columbia, lodgepole pine (Pinus contorta) remained dominant and became more productive with exogenous climate forcing as temperature, nutrient, and flooding limitations decreased. These case studies demonstrate a new framework for forest modeling and emphasize the advantages of integrating remotely sensed data with mechanistic models, thereby laying groundwork for future research that explores near-term impacts of non-stationary ecological change.

Inferring the tree regeneration niche from inventory data using a dynamic forest model

Abstract. The regeneration niche of trees is governed by many processes and factors that are challenging to determine. Besides a species's geographic distribution, which determines if seeds are available, a myriad of local processes in forest ecosystems (e.g., competition and pathogens) exert influences on tree regeneration. Consequently, the representation of tree regeneration in dynamic forest models is a notoriously complicated process which often involves many subprocesses that are often data deficient. The ForClim forest gap model solved this problem by linking species traits to regeneration properties. However, this regeneration module was never validated with large-scale data. Here, we compare this trait-based approach with an inverse calibration approach where we estimate regeneration parameters directly from a large dataset of unmanaged European forests. The inverse calibration was done using Bayesian inference, estimating shade and drought tolerance as well as the temperature requirements for 11 common tree species along with the intensity of regeneration (i.e., the maximum regeneration rate). We find that the parameters determining the species' light niche (i.e., light requirements) are similar for the trait-based and calibrated values for both model variants, but only a more complex model variant that included competition between recruits leads to plausible estimates of the drought niche. The trait-derived temperature niche did not match to the estimates from either model variant using inverse calibration. The parameter estimates differed between the complex and the simple model, with the estimates for the complex model being closer to the trait-based parameters. In both model variants, the calibration strongly changed the parameters that determine regeneration intensity compared to the default. We conclude that the regeneration niche of trees can be recovered from a large forestry dataset in terms of the stand-level parameters light availability and regeneration intensity, while abiotic drivers (temperature and drought) are more elusive. The higher performance (better fit to hold out) of the inversely calibrated models underpins the importance of informing dynamic models by real-world observations. Future research should focus on even greater environmental coverage of observations of demographic processes in unmanaged forests to verify our findings at species range limits under extreme climatic conditions.

Identifying compound weather drivers of forest biomass loss with generative deep learning

Abstract Globally, forests are net carbon sinks that partly mitigates anthropogenic climate change. However, there is evidence of increasing weather-induced tree mortality, which needs to be better understood to improve forest management under future climate conditions. Disentangling drivers of tree mortality is challenging because of their interacting behavior over multiple temporal scales. In this study, we take a data-driven approach to the problem. We generate hourly temperate weather data using a stochastic weather generator to simulate 160,000 years of beech, pine, and spruce forest dynamics with a forest gap model. These data are used to train a generative deep learning model (a modified variational autoencoder) to learn representations of three-year-long monthly weather conditions (precipitation, temperature, and solar radiation) in an unsupervised way. We then associate these weather representations with years of high biomass loss in the forests and derive weather prototypes associated with such years. The identified prototype weather conditions are associated with 5–22% higher median biomass loss compared to the median of all samples, depending on the forest type and the prototype. When prototype weather conditions co-occur, these numbers increase to 10–25%. Our research illustrates how generative deep learning can discover compounding weather patterns associated with extreme impacts.

A new approach to derive productivity of tropical forests using radar remote sensing measurements.

Deriving gross & net primary productivity (GPP & NPP) and carbon turnover time of forests from remote sensing remains challenging. This study presents a novel approach to estimate forest productivity by combining radar remote sensing measurements, machine learning and an individual-based forest model. In this study, we analyse the role of different spatial resolutions on predictions in the context of the Radar BIOMASS mission (by ESA). In our analysis, we use the forest gap model FORMIND in combination with a boosted regression tree (BRT) to explore how spatial biomass distributions can be used to predict GPP, NPP and carbon turnover time (τ) at different resolutions. We simulate different spatial biomass resolutions (4 ha, 1 ha and 0.04 ha) in combination with different vertical resolutions (20, 10 and 2 m). Additionally, we analysed the robustness of this approach and applied it to disturbed and mature forests. Disturbed forests have a strong influence on the predictions which leads to high correlations (R2 > 0.8) at the spatial scale of 4 ha and 1 ha. Increased vertical resolution leads generally to better predictions for productivity (GPP & NPP). Increasing spatial resolution leads to better predictions for mature forests and lower correlations for disturbed forests. Our results emphasize the value of the forthcoming BIOMASS satellite mission and highlight the potential of deriving estimates for forest productivity from information on forest structure. If applied to more and larger areas, the approach might ultimately contribute to a better understanding of forest ecosystems.

Long-term dynamics of tree of heaven (Ailanthus altissima) in central European forests

Since the 1950s, the tree of heaven (Ailanthus altissima) has progressively invaded forests in southern Switzerland and is becoming a growing concern also north of the Alps. Recent studies have increased the understanding of the species’ ecology, but its role in long-term stand dynamics remains uncertain. Therefore, we simulated the long-term dynamics of unmanaged and managed forest stands in southern and northern Switzerland under current and future climate conditions (RCP8.5) using the forest gap model ForClim. Our results indicate that A. altissima will increase its presence in the short term (< 100 yrs), but does not gain dominance in the long term (> 200 yrs), confirming its pioneer character. Timber harvesting led to an increasing share of A. altissima compared to unmanaged stands. Overall, our findings suggest that in the long run, a competitive displacement of native dominant species by A. altissima appears unlikely, with the exception of drought-prone sites under strong climate change. Furthermore, our findings underline the importance of the frequency and intensity of forest management for the long-term abundance of A. altissima in forest stands.

Multi-model approach to integrate climate change impact on carbon sequestration potential of afforestation scenarios in Quebec, Canada

Afforestation of unproductive or currently non-forested territories can increase carbon land sinks and thus contribute to mitigate climate change. However, investments on large-scale afforestation could be risky because of the predicted effect of climate change on forest productivity of newly created plantations. The aim of this study was to assess the carbon sequestration and mitigation potential of afforestation scenarios with different species (Picea mariana, Picea glauca, Pinus banksiana, Pinus resinosa and Populus spp) on open woodlands and abandoned farmlands in the Province of Quebec (Canada) under different radiative forcing projections. We modelled carbon dynamics in these lands under three Representative Concentration Pathways projections (RCP 2.6, RCP 4.5, and RCP 8.5) over the 2021–2100 period. The forest gap model PICUS was used to model tree growth of afforested species as a function of the Representative Concentration Pathways 2.6, 4.5 and 8.5; these data were then used as input in the Carbon Budget Model – Canadian Forest Sector 3 to simulate the evolution of ecosystem carbon stocks and fluxes as a function of forest management and climate. Carbon transfer to harvested wood products, and carbon fluxes associated with product life cycles and substitution effects on markets, were also included in the analyses. Results showed that Pinus species responded more strongly to variations in radiative forcing than for the other simulated species. Overall, aboveground biomass was particularly altered by increased radiative forcing, which in turn reduced harvesting yield and transfers to wood processing and products. At the end of the simulation, despite the expected impacts of radiative forcing on ecosystems, afforestation scenarios on open woodlands with black spruce, white spruce, and jack pine can deliver carbon mitigation of 32% – 70% over the baseline scenario and 4% – 12% for red pine on abandoned farmlands and, hence, contribute to efforts to reduce GHG emissions, especially over the long term. Although climate change is expected to impact the growth of newly planted areas as part of afforestation efforts, the results of our study suggest that the choice of species to plant and the selected forest management strategy have a greater impact on carbon stocks than climate change itself. This study provides a better understanding of the dynamics of afforestation under climate change and whether investments in plantation can contribute to GHG reduction targets.

Functionally distinct tree species support long-term productivity in extreme environments.

Despite evidence of a positive effect of functional diversity on ecosystem productivity, the importance of functionally distinct species (i.e. species that display an original combination of traits) is poorly understood. To investigate how distinct species affect ecosystem productivity, we used a forest-gap model to simulate realistic temperate forest successions along an environmental gradient and measured ecosystem productivity at the end of the successional trajectories. We performed 10 560 simulations with different sets and numbers of species, bearing either distinct or indistinct functional traits, and compared them to random assemblages, to mimic the consequences of a regional loss of species. Long-term ecosystem productivity dropped when distinct species were lost first from the regional pool of species, under the harshest environmental conditions. On the contrary, productivity was more dependent on ordinary species in milder environments. Our findings show that species functional distinctiveness, integrating multiple trait dimensions, can capture species-specific effects on ecosystem productivity. In a context of an environmentally changing world, they highlight the need to investigate the role of distinct species in sustaining ecosystem processes, particularly in extreme environmental conditions.

Bridging implementation gaps to connect large ecological datasets and complex models.

Merging robust statistical methods with complex simulation models is a frontier for improving ecological inference and forecasting. However, bringing these tools together is not always straightforward. Matching data with model output, determining starting conditions, and addressing high dimensionality are some of the complexities that arise when attempting to incorporate ecological field data with mechanistic models directly using sophisticated statistical methods. To illustrate these complexities and pragmatic paths forward, we present an analysis using tree‐ring basal area reconstructions in Denali National Park (DNPP) to constrain successional trajectories of two spruce species (Picea mariana and Picea glauca) simulated by a forest gap model, University of Virginia Forest Model Enhanced—UVAFME. Through this process, we provide preliminary ecological inference about the long‐term competitive dynamics between slow‐growing P. mariana and relatively faster‐growing P. glauca. Incorporating tree‐ring data into UVAFME allowed us to estimate a bias correction for stand age with improved parameter estimates. We found that higher parameter values for P. mariana minimum growth under stress and P. glauca maximum growth rate were key to improving simulations of coexistence, agreeing with recent research that faster‐growing P. glauca may outcompete P. mariana under climate change scenarios. The implementation challenges we highlight are a crucial part of the conversation for how to bring models together with data to improve ecological inference and forecasting.

Challenges to aboveground biomass prediction from waveform lidar

Accurate accounting of aboveground biomass density (AGBD) is crucial for carbon cycle, biodiversity, and climate change science. The Global Ecosystem Dynamics Investigation (GEDI), which maps global AGBD from waveform lidar, is the first of a new generation of Earth observation missions designed to improve carbon accounting. This paper explores the possibility that lidar waveforms may not be unique to AGBD—that forest stands with different AGBD may produce highly similar waveforms—and we hypothesize that non-uniqueness may contribute to the large uncertainties in AGBD predictions. Our analysis integrates simulated GEDI waveforms from 428 in situ stem maps with output from an individual-based forest gap model, which we use to generate a database of potential forest stands and simulate GEDI waveforms from those stands. We use this database to predict the AGBD of the 428 in situ stem maps via two different methods: a linear regression from waveform metrics, and a waveform-matching approach that accounts for waveform-AGBD non-uniqueness. We find that some in situ waveforms are more unique to AGBD than others, which notably impacts AGBD prediction uncertainty (7–411 Mg ha−1, average of 167 Mg ha−1). We also find that forest structure complexity may influence the non-uniqueness effect; stands with low structural complexity are more unique to AGBD than more mature stands with multiple cohorts and canopy layers. These findings suggest that the non-uniqueness phenomena may be introduced by the measuring characteristics of waveform lidar in combination with how forest structure manifests at small scales, and we discuss how this complexity may complicate uncertainty estimation in AGBD prediction. This analysis suggests a limit to the accuracy and precision of AGBD predictions from lidar waveforms seen in empirical studies, and underscores the need for further exploration of the relationships between lidar remote sensing measurements, forest structure, and AGBD.

Magnitude and timing of density reduction are key for the resilience to severe drought in conifer-broadleaf mixed forests in Central Europe

Key messageWe applied a modified forest gap model (ForClim) to depict changes in stand water transpiration via density reduction as a forest adaptation strategy. This approach is the key to analyzing the ecological resilience to drought, stress-induced mortality, and economic efficiency of managed mixed forest stands in Central Europe. The results show that specific geographic conditions and forest composition define the optimal stand density of drought-resilient forests.ContextReducing stand density has been recognized as a valid strategy to increase forest resilience to drought. Moreover, to develop adaptive management strategies (AMS) under climate change, it is crucial to consider not only drought resilience but also the economic efficiency of alternative AMS proposed to alleviate drought effects.AimsTo analyze how decreased inter-tree competition among overstorey trees affects stand vulnerability to drought and its expected yield.MethodsWe integrated experimental thinning data and historical responses to drought years in a climate-sensitive forest gap model, ForClim. We tested a business as usual (BAU) and three alternative AMS (“do-nothing,” low- and high-intensity overstorey removal) in mixed stands of Norway spruce (Picea abies), silver fir (Abies alba), and European beech (Fagus sylvatica) along an elevational gradient of 520–1020 m a.s.l. in Central Europe.ResultsHigh-intensity overstorey removal in mixed stands of all three species considerably increased forest volume growth resilience to drought and decreased stress-induced mortality by two-thirds vis à vis a “do-nothing” strategy. In sites including only conifer species, forest resilience was equally improved by high- and low-intensity overstorey removal compared to that in the BAU strategy. Regarding the timber economy, high-intensity overstorey removal resulted in a higher economic revenue of mixed stands (~ 22% higher net present value than other strategies) on the high-elevation sites (> 1000 m a.s.l.).ConclusionModifying forest density and structure by overstorey removal is principally suitable to increase forest resilience to drought and improve its economic efficiency. The magnitude of the effect however depends on the geographical setting and forest composition.

Mixed Regional Shifts in Conifer Productivity under 21st-Century Climate Projections in Canada’s Northeastern Boreal Forest

Models of forest growth and yield (G&amp;Y) are a key component in long-term strategic forest management plans. Models leveraging the industry-standard “empirical” approach to G&amp;Y are frequently underpinned by an assumption of historical consistency in climatic growing conditions. This assumption is problematic as forest managers look to obtain reliable growth predictions under the changing climate of the 21st century. Consequently, there is a pressing need for G&amp;Y modelling approaches that can be more robustly applied under the influence of climate change. In this study we utilized an established forest gap model (JABOWA-3) to simulate G&amp;Y between 2020 and 2100 under Representative Concentration Pathways (RCP) 2.6, 4.5, and 8.5 in the Canadian province of Newfoundland and Labrador (NL). Simulations were completed using the province’s permanent sample plot data and surface-fitted climatic datasets. Through model validation, we found simulated basal area (BA) aligned with observed BA for the major conifer species components of NL’s forests, including black spruce [Picea mariana (Mill.) Britton et al.] and balsam fir [Abies balsamea (L.) Mill]. Model validation was not as robust for the less abundant species components of NL (e.g., Acer rubrum L. 1753, Populus tremuloides Michx., and Picea glauca (Moench) Voss). Our simulations generally indicate that projected climatic changes may modestly increase black spruce and balsam fir productivity in the more northerly growing environments within NL. In contrast, we found productivity of these same species to only be maintained, and in some instances even decline, toward NL’s southerly extents. These generalizations are moderated by species, RCP, and geographic parameters. Growth modifiers were also prepared to render empirical G&amp;Y projections more robust for use under periods of climate change.

Beyond forest succession: A gap model to study ecosystem functioning and tree community composition under climate change

Abstract Climate change impacts forest functioning and dynamics, and large uncertainties remain regarding the interactions between species composition, demographic processes and environmental drivers. There are few robust tools available to link these processes, which precludes accurate projections and recommendations for long‐term forest management. Forest gap models present a balance between complexity and generality and are widely used in predictive forest ecology. However, their relevance to tackle questions about the links between species composition, climate and forest functioning is unclear. In this regard, demonstrating the ability of gap models to predict the growth of forest stands at the annual parameterization scale resolution—representing a sensitive and integrated signal of tree functioning and mortality risk—appears as a fundamental step. In this study, we aimed at assessing the ability of a gap model to accurately predict forest growth in the short term and potential community composition in the long term, across a wide range of species and environmental conditions. To do so, we present the gap model ForCEEPS, calibrated using an original parameterization procedure for the main tree species in France. ForCEEPS was shown to satisfactorily predict forest annual growth (averaged over a few years) at the plot level from mountain to Mediterranean climates, regardless of the species. Such an accuracy was not gained at the cost of losing precision for long‐term predictions, as the model showed a strong ability to predict potential community compositions. The mechanistic relevance of ForCEEPS parameterization was explored by showing the congruence between the values of key model parameter and species functional traits. We further showed that accounting for the spatial configuration of crowns within forest stands, the effects of climatic constraints and the variability of shade tolerances in the species community are all crucial to better predict short‐term productivity with gap models. Synthesis. The dual ability of predicting short‐term functioning and long‐term community composition, as well as the balance between generality and realism (i.e. predicting accuracy) of the new generation of gap models may open great perspectives for the exploration of the biodiversity–ecosystem functioning relationships, species coexistence mechanisms and the impacts of climate change on forest ecosystems. A free Plain Language Summary can be found within the Supporting Information of this article.

Using Microwave Profile Radar to Estimate Forest Canopy Leaf Area Index: Linking 3D Radiative Transfer Model and Forest Gap Model

Profile radar allows direct characterization of the vertical forest structure. Short-wavelength, such as Ku or X band, microwave data provide opportunities to detect the foliage. In order to exploit the potential of radar technology in forestry applications, a helicopter-borne Ku-band profile radar system, named Tomoradar, has been developed by the Finnish Geospatial Research Institute. However, how to use the profile radar waveforms to assess forest canopy parameters remains a challenge. In this study, we proposed a method by matching Tomoradar waveforms with simulated ones to estimate forest canopy leaf area index (LAI). Simulations were conducted by linking an individual tree-based forest gap model ZELIG and a three-dimension (3D) profile radar simulation model RAPID2. The ZELIG model simulated the parameters of potential local forest succession scene, and the RAPID2 model utilized the parameters to generate 3D virtual scenes and simulate waveforms based on Tomoradar configuration. The direct comparison of simulated and collected waveforms from Tomoradar could be carried out, which enabled the derivation of possible canopy LAI distribution corresponding to the Tomoradar waveform. A 600-m stripe of Tomoradar data (HH polarization) collected in the boreal forest at Evo in Finland was used as a test, which was divided into 60 plots with an interval of 10 m along the trajectory. The average waveform of each plot was employed to estimate the canopy LAI. Good results have been found in the waveform matching and the uncertainty of canopy LAI estimation. There were 95% of the plots with the mean relative overlapping rate (RO) above 0.7. The coefficients of variation of canopy LAI estimates were less than 0.20 in 80% of the plots. Compared to lidar-derived canopy effective LAI estimation, the coefficient of determination was 0.46, and the root mean square error (RMSE) was 1.81. This study established a bridge between the Ku band profile radar waveform and the forest canopy LAI by linking the RAPID2 and ZELIG model, presenting the uncertainty of forest canopy LAI estimation using Tomoradar. It is worth noting that since the difference of backscattering contribution is caused by both canopy structure and tree species, similar waveforms may correspond to different canopy LAI, inducing the uncertainty of canopy LAI estimation, which should be noticed in forest parameters estimation with empirical methods.

Modelling Climate–Sensitive Forest Succession to Assess Impacts of Climate Change and Support Decision Making

Mountain forests provide a wide variety of ecosystem services to society. In light of the trend of temperature increase and related climatic extremes in the Greater Alpine Area, mountain forests are likely to undergo dramatic changes in the coming centuries. Therefore, forest managers face the challenge of adapting forests to support resilience to climate change. To facilitate this process, the forest gap model ForClim, a process-based forest succession model, was applied to generate site-specific information on future forest stand development and species composition. The tree species composition without management activities was predicted up to 2100 assuming a stable climate, and for both moderate and severe climate-change scenarios. Furthermore, three different forest stand development scenarios were implemented. The forest stand investigated in our research shows significant climate-sensitivity. Results demonstrate that deciduous mixed forest stands are necessary to increase resilience and manage forests for climate change. Using active silvicultural measures, such as assisted migration through planting thermophilus species, negative effects of climate change could be reduced. The modelling approach presented here is appropriate for assessing the impacts of climate change and supporting decision making by local forest managers.

Using forest gap models and experimental data to explore long-term effects of tree diversity on the productivity of mixed planted forests

Key messageIn this exploratory study, we show how combining the strength of tree diversity experiment with the long-term perspective offered by forest gap models allows testing the mixture yielding behavior across a full rotation period. Our results on a SW France example illustrate how mixing maritime pine with birch may produce an overyielding (i.e., a positive net biodiversity effect).ContextUnderstanding the link between tree diversity and stand productivity is a key issue at a time when new forest management methods are investigated to improve carbon sequestration and climate change mitigation. Well-controlled tree diversity experiments have been set up over the last decades, but they are still too young to yield relevant results from a long-term perspective. Alternatively, forest gap models appear as appropriate tools to study the link between diversity and productivity as they can simulate mixed forest growth over an entire forestry cycle.AimsWe aimed at testing whether a forest gap model could first reproduce the results from a tree diversity experiment, using its plantation design as input, and then predict the species mixing effect on productivity and biomass in the long term.MethodsHere, we used data from different forest experimental networks to calibrate the gap model ForCEEPS for young pine (Pinus pinaster) and birch (Betula pendula) stands. Then, we used the refined model to compare the productivity of pure and mixed pine and birch stands over a 50-year cycle. The mixing effect was tested for two plantation designs, i.e., species substitution and species addition, and at two tree densities.ResultsRegarding the comparison with the experiment ORPHEE (thus on the short term), the model well reproduced the species interactions observed in the mixed stands. Simulations showed an overyielding (i.e., a positive net biodiversity effect) in pine-birch mixtures in all cases and during the full rotation period. A transgressive overyielding was detected in mixtures resulting from birch addition to pine stands at low density. These results were mainly due to a positive mixing effect on pine growth being larger than the negative effect on birch growth.ConclusionAlthough this study remains explorative, calibrating gap models with data from monospecific stands and validating with data from the manipulative tree diversity experiment (ORPHEE) offers a powerful tool for further investigation of the productivity of forest mixtures. Improving our understanding of how abiotic and biotic factors, including diversity, influence the functioning of forest ecosystems should help to reconsider new forest managements optimizing ecosystem services.

Evaluating individual-based tree mortality modeling with temporal observation data collected from a large forest plot

In forests, tree mortality strongly determines forest dynamics, creates gaps for recruitment and contributes to the coexistence of tree species. Individual-based models (IBMs) of tree mortality are key submodels in forest gap models and have been shown to drive simulated long-term forest dynamics. However, tree mortality IBMs remain poorly evaluated at the stand scale, particularly quantitatively, due to the lack of adequate tree mortality demographic data. Tree mortality dynamic data have often been absent from previous mortality IBMs, resulting in difficulty during model evaluation at the stand scale. The goals of this study were (1) to develop a spatially-explicit tree mortality IBM derived from the FORSKA forest gap model and (2) to evaluate its performance by both qualitative and quantitative fits based on a 10-year interval of tree mortality demographic data under a 9-ha forest dynamics plot (FDP) in an old-growth temperate forest in Northeast China. The results showed that, for model qualitative evaluation, the observed dead trees were mainly distributed in the southwest and northeast parts, but the predicted dead trees were centrally clustered only in the southwest part of the FDP. For model quantitative evaluation, the overall model error (Error (%)) for all trees was −10.5%. However, the absolute values of model error for some species (i.e., Acer spp., Betula spp., Tilia spp., Fraxinus mandschurica, and Ulmus spp.) and some diameter at breast height (DBH) classes (i.e., 30–40, 40–50, 50–60, 60–70, and ≥70 cm) were greater than or equal to 20%. Our study highlights that the tree mortality IBM at the stand scale still requires further improvement to enhance the model performance and that the FDPs could provide crucial data support for initialization, parameterization and evaluation in the tree mortality IBM at the stand scale in forest dynamic modeling.

Consequences of a Reduced Number of Plant Functional Types for the Simulation of Forest Productivity

Tropical forests represent an important pool in the global carbon cycle. Their biomass stocks and carbon fluxes are variable in space and time, which is a challenge for accurate measurements. Forest models are therefore used to investigate these complex forest dynamics. The challenge of considering the high species diversity of tropical forests is often addressed by grouping species into plant functional types (PFTs). We investigated how reduced numbers of PFTs affect the prediction of productivity (GPP, NPP) and other carbon fluxes derived from forest simulations. We therefore parameterized a forest gap model for a specific study site with just one PFT (comparable to global vegetation models) on the one hand, and two versions with a higher amount of PFTs, on the other hand. For an old-growth forest, aboveground biomass and basal area can be reproduced very well with all parameterizations. However, the absence of pioneer tree species in the parameterizations with just one PFT leads to a reduction in estimated gross primary production by 60% and an increase of estimated net ecosystem exchange by 50%. These findings may have consequences for productivity estimates of forests at regional and continental scales. Models with a reduced number of PFTs are limited in simulating forest succession, in particular regarding the forest growth after disturbances or transient dynamics. We conclude that a higher amount of species groups increases the accuracy of forest succession simulations. We suggest using at a minimum three PFTs with at least one species group representing pioneer tree species.

Species composition and forest structure explain the temperature sensitivity patterns of productivity in temperate forests

Abstract. Rising temperatures due to climate change influence the wood production of forests. Observations show that some temperate forests increase their productivity, whereas others reduce their productivity. This study focuses on how species composition and forest structure properties influence the temperature sensitivity of aboveground wood production (AWP). It further investigates which forests will increase their productivity the most with rising temperatures. We described forest structure by leaf area index, forest height and tree height heterogeneity. Species composition was described by a functional diversity index (Rao's Q) and a species distribution index (ΩAWP). ΩAWP quantified how well species are distributed over the different forest layers with regard to AWP. We analysed 370 170 forest stands generated with a forest gap model. These forest stands covered a wide range of possible forest types. For each stand, we estimated annual aboveground wood production and performed a climate sensitivity analysis based on 320 different climate time series (of 1-year length). The scenarios differed in mean annual temperature and annual temperature amplitude. Temperature sensitivity of wood production was quantified as the relative change in productivity resulting from a 1 ∘C rise in mean annual temperature or annual temperature amplitude. Increasing ΩAWP positively influenced both temperature sensitivity indices of forest, whereas forest height showed a bell-shaped relationship with both indices. Further, we found forests in each successional stage that are positively affected by temperature rise. For such forests, large ΩAWP values were important. In the case of young forests, low functional diversity and small tree height heterogeneity were associated with a positive effect of temperature on wood production. During later successional stages, higher species diversity and larger tree height heterogeneity were an advantage. To achieve such a development, one could plant below the closed canopy of even-aged, pioneer trees a climax-species-rich understorey that will build the canopy of the mature forest. This study highlights that forest structure and species composition are both relevant for understanding the temperature sensitivity of wood production.

Global sensitivity analysis of a dynamic vegetation model: Model sensitivity depends on successional time, climate and competitive interactions

Accurately predicting the dynamics of tree species productivity as well as their ranges at large scales is of key importance for assessing the impact of global change on forests. Dynamic vegetation models (DVMs), particularly forest gap models (FGMs), have been suggested as suitable tools for such joint predictions. However, DVMs generally feature a large number of parameters that need to be estimated and may cause considerable uncertainty in model outputs. In addition, model sensitivity may depend on environmental conditions, stand composition and development stage. We systematically evaluated the parameter sensitivity on simulated basal area of the state-of-the art FGM ForClim along a wide ecological gradient to analyze model behavior and identify key parameters and processes that cause the highest variability in model output. We applied the revised Morris screening method 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 (dynamic equilibrium). The most influential parameters were related to tree establishment, the water and light regimes, growth and temperature, whereby the relative parameter influence of the latter strongly varied with climate. Further, model sensitivity differed between monospecific and mixed stands as well as between early and late succession, reflecting the differential influence of ecological processes with stand structure. We conclude that the parameter sensitivity of complex models should be analyzed individually for several system states of interest. We recommend to focus the further development (process representation and calibration) and analysis of FGMs on process representations related to establishment, water limitations and phenology to improve the robustness of model predictions. We provide recommendations for specific improvements of FGMs to better represent range dynamics.

Spatial heterogeneity of biomass and forest structure of the Amazon rain forest: Linking remote sensing, forest modelling and field inventory

AbstractAimEstimating the current spatial variation of biomass in the Amazon rain forest is a challenge and remains a source of substantial uncertainty in the assessment of the global carbon cycle. Precise estimates need to consider small‐scale variations of forest structures resulting from local disturbances, on the one hand, and require large‐scale information on the state of the forest that can be detected by remote sensing, on the other hand. In this study, we introduce a novel method that links a forest gap model and a canopy height map to derive the biomass distribution of the Amazon rain forest.LocationAmazon rain forest.MethodsAn individual‐based forest model was applied to estimate the variation of aboveground biomass across the Amazon rain forest. The forest model simulated individual trees; hence, it allowed the direct comparison of simulated and observed canopy heights from remote sensing. The comparison enabled the detection of disturbed forest states and the derivation of a simulation‐based biomass map at 0.16 ha resolution.ResultsSimulated biomass values ranged from 20 to 490 t (dry mass)/ha across 7.8 Mio km2 of Amazon rain forest. We estimated a total aboveground biomass stock of 76 GtC, with a coefficient of variation of 45%. We found mean differences of only 15% when comparing biomass values of the map with 114 field inventories. The forest model enables the derivation of additional estimates, such as basal area and stem density.Main conclusionsLinking a canopy height map with an individual‐based forest model captures the spatial variation of biomass in the Amazon rain forest at high resolution. The study demonstrates how this linkage allows for quantifying the spatial variation in forest structure caused by tree‐level to regional‐scale disturbances. It thus provides a basis for large‐scale analyses on the heterogeneous structure of tropical forests and their carbon cycle.