Tree mortality submodels drive simulated long-term forest dynamics: assessing 15 models from the stand to global scale

Divergent regional volume growth responses of Scots Pine and Oak stands to climate change in Europe

European climatic change has been proposed to induce many changes to forests, about factors such as tree species distributions, site productivity, groundwater availability, outbreaks of forest pests, and damage from wind-breakage of trees. Novel approaches to empirical tree growth modelling using re-measurements over large climatic gradients capture variability associated with long-term climatic conditions as well as weather during the growth period. Using the latest version of the individual tree-based forest simulator, PrognAus, which has been outfitted with a climate-sensitive basal area increment module, we forecast growth of trees in pure and mixed stands of Pinus sylvestris L. and Quercus spp. across a network of 23 European sites between 2017 and 2100 under current climate and RCP 4.5 and RCP 8.5 climatic scenarios. By training a stand-level static reduced model (SRM) from these local level results, we forecast widespread future growth changes for stands of Pinus sylvestris and Quercus spp. across Europe. Our SRM predicts stand gross-volume relative growth (ratio of the gross volume production in a given growth year to the gross volume production until the start of the growth year) with a generalized additive mixed model (GAMM). We decomposed overall growth into tensors capturing variation associated with stand species mixture type (pure P. sylvestris, mixed P. sylvestris- Q. spp., pure Q. spp.), age, and weather conditions during the growth year and the preceeding year. Wall-to-wall predictions based on the SRM are presented for a high-resolution 30-arcsecond grid spanning most parts of Europe.

Source vs sink limitations on tree growth: from physiological mechanisms to evolutionary constraints and terrestrial carbon cycle implications

The potential for widespread sink-limited plant growth has received increasing attention in the literature in the past few years. Despite recent evidence for sink limitations to plant growth, there are reasons to be cautious about a sink-limited world view. First, source-limited vegetation models do a reasonable job at capturing geographic patterns in plant productivity and responses to resource limitations. Second, from an evolutionary perspective, it is nonadaptive for plants to invest in increasing carbon assimilation if growth is primarily sink-limited. In this review, we synthesize the potential evidence for and underlying physiology of sink limitation across terrestrial ecosystems and contrast mechanisms of sink limitation with those of source-limited productivity. We highlight evolutionary restrictions on the magnitude of sink limitation at the organismal level. We also detail where mechanisms regulating sink limitation at the organismal and ecosystem scale (e.g. the terrestrial carbon sink) diverge. Although we find that there is currently no direct evidence for widespread organismal sink limitation, we propose a series of follow-up growth chamber manipulations, systematized measurements, and modeling experiments targeted at diagnosing nonadaptive buildup of excess nonstructural carbohydrates that will help illuminate the prevalence and magnitude of organismal sink limitation.

Adaptive forest management improves stand-level resilience of temperate forests under multiple stressors

Forests are expected to be strongly affected by modifications in climate and disturbance regimes, threatening their ability to sustain the provision of essential services. Promoting drought-tolerant species or functionally diverse stands have recently emerged as management options to cope with global change. Our study aimed at evaluating the impact of contrasting stand-level management scenarios on the resilience of temperate forests in eastern North America and central-western Europe using the individual process-based model HETEROFOR. We simulated the evolution of eight stands over 100 years under a future extreme climate according to four management scenarios (business as usual - BAU; climate change adaptation - CC; functional diversity approach - FD; no management - NM) while facing multiple disturbances, resulting in a total of 160 simulations. We found that FD demonstrated the greatest resilience regarding transpiration and tree biomass, followed by CC and then BAU, while these three scenarios were equivalent concerning the net primary production. These results were however dependent on forest type: increasing functional diversity was a powerful option to increase the resilience of coniferous plantations whereas no clear differences between BAU and adaptive management scenarios were detected in broadleaved and mixed stands. The FD promoted a higher level of tree species diversity than any other scenario, and all scenarios of management were similar regarding the amount of harvested wood. The NM always showed the lowest resilience, demonstrating that forest management could be an important tool to mitigate adverse effects of global change. Our study highlighted that tree-level process-based models are a relevant tool to identify suitable management options for adapting forests to global change provided that model limitations are considered, and that alternative management options, particularly those based on functional diversity, are promising and should be promoted from now on.

Tall Bornean forests experience higher canopy disturbance rates than those in the eastern Amazon or Guiana shield

The future of tropical forests hinges on the balance between disturbance rates, which are expected to increase with climate change, and tree growth. Whereas tree growth is a slow process, disturbance events occur sporadically and tend to be short-lived. This difference challenges forest monitoring to achieve sufficient resolution to capture tree growth, while covering the necessary scale to characterize disturbance rates. Airborne LiDAR time series can address this challenge by measuring landscape scale changes in canopy height at 1 m resolution. In this study, we present a robust framework for analysing disturbance and recovery processes in LiDAR time series data. We apply this framework to 8000 ha of old-growth tropical forests over a 4-5-year time frame, comparing growth and disturbance rates between Borneo, the eastern Amazon and the Guiana shield. Our findings reveal that disturbance was balanced by growth in eastern Amazonia and the Guiana shield, resulting in a relatively stable mean canopy height. In contrast, tall Bornean forests experienced a decrease in canopy height due to numerous small-scale (<0.1 ha) disturbance events outweighing the gains due to growth. Within sites, we found that disturbance rates were weakly related to topography, but significantly increased with maximum canopy height. This could be because taller trees were particularly vulnerable to disturbance agents such as drought, wind and lightning. Consequently, we anticipate that tall forests, which contain substantial carbon stocks, will be disproportionately affected by the increasing severity of extreme weather events driven by climate change.

Residual water losses mediate the trade-off between growth and drought survival across saplings of 12 tropical rainforest tree species with contrasting hydraulic strategies

Knowledge of the physiological mechanisms underlying species vulnerability to drought is critical for better understanding patterns of tree mortality. Investigating plant adaptive strategies to drought should thus help to fill this knowledge gap, especially in tropical rainforests exhibiting high functional diversity. In a semi-controlled drought experiment using 12 rainforest tree species, we investigated the diversity in hydraulic strategies and whether they determined the ability of saplings to use stored non-structural carbohydrates during an extreme imposed drought. We further explored the importance of water- and carbon-use strategies in relation to drought survival through a modelling approach. Hydraulic strategies varied considerably across species with a continuum between dehydration tolerance and avoidance. During dehydration leading to hydraulic failure and irrespective of hydraulic strategies, species showed strong declines in whole-plant starch concentrations and maintenance, or even increases in soluble sugar concentrations, potentially favouring osmotic adjustments. Residual water losses mediated the trade-off between time to hydraulic failure and growth, indicating that dehydration avoidance is an effective drought-survival strategy linked to the 'fast-slow' continuum of plant performance at the sapling stage. Further investigations on residual water losses may be key to understanding the response of tropical rainforest tree communities to climate change.

Plasticity and implications of water-use traits in contrasting tropical tree species under climate change

Plants face a trade-off between hydraulic safety and growth, leading to a range of water-use strategies in different species. However, little is known about such strategies in tropical trees and whether different water-use traits can acclimate to warming. We studied five water-use traits in 20 tropical tree species grown at three different altitudes in Rwanda (RwandaTREE): stomatal conductance (gs), leaf minimum conductance (gmin), plant hydraulic conductance (Kplant), leaf osmotic potential (ψo) and net defoliation during drought. We also explored the links between these traits and growth and mortality data. Late successional (LS) species had low Kplant, gs and gmin and, thus, low water loss, while low ψo helped improve leaf water status during drought. Early successional (ES) species, on the contrary, used more water during both moist and dry conditions and exhibited pronounced drought defoliation. The ES strategy was associated with lower mortality and more pronounced growth enhancement at the warmer sites compared to LS species. While Kplant and gmin showed downward acclimation in warmer climates, ψo did not acclimate and gs measured at prevailing temperature did not change. Due to distinctly different water use strategies between successional groups, ES species may be better equipped for a warmer climate as long as defoliation can bridge drought periods.

"Mortality, or not mortality, that is the question …": How to Treat Removals in Tree Survival Analysis of Central European Managed Forests

Tree mortality is an objective forest health criterion and is particularly suitable for long-term and large-scale studies of forest condition. However, it is impossible to determine actual tree mortality in Central European managed forests where trees are removed for various reasons. In this case, the only way to approximate tree mortality is to define the range in which it occurs. This can be carried out by including in the mortality calculations either dead trees that remain in the stand at the end of the assessment period or additionally trees that have been removed from the stand. We used data from the annual forest monitoring surveys in Poland from 2009 to 2022 for pine, spruce, oak and birch to perform a survival analysis in which we included all removals or sanitary cuttings either as censored or complete observations. The differences between the calculated mortality rates were significant, indicating the importance of how removals are treated in the analysis. To assess which method used for mortality calculation was more appropriate, we compared values for last recorded defoliation and severity of damage from live, dead and thinned or salvaged trees. For all species studied, significant differences were found between dead trees or trees removed by sanitation cuts and living trees or trees removed by thinning, suggesting that not only dead trees remaining in the forest, but also trees removed by sanitation cuts, should be considered when calculating mortality in managed stands. We also recommend the use of survival analysis in forest monitoring as a routine method for assessing the health of stands.

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.

Balancing disturbance risk and ecosystem service provisioning in Swiss mountain forests: an increasing challenge under climate change

Climate change severely affects mountain forests and their ecosystem services, e.g., by altering disturbance regimes. Increasing timber harvest (INC) via a close-to-nature forestry may offer a mitigation strategy to reduce disturbance predisposition. However, little is known about the efficiency of this strategy at the scale of forest enterprises and potential trade-offs with biodiversity and ecosystem services (BES). We applied a decision support system which accounts for disturbance predisposition and BES indicators to evaluate the effect of different harvest intensities and climate change scenarios on windthrow and bark beetle predisposition in a mountain forest enterprise in Switzerland. Simulations were carried out from 2010 to 2100 under historic climate and climate change scenarios (RCP4.5, RCP8.5). In terms of BES, biodiversity (structural and tree species diversity, deadwood amount) as well as timber production, recreation (visual attractiveness), carbon sequestration, and protection against gravitational hazards (rockfall, avalanche and landslides) were assessed. The INC strategy reduced disturbance predisposition to windthrow and bark beetles. However, the mitigation potential for bark beetle disturbance was relatively small (- 2.4%) compared to the opposite effect of climate change (+ 14% for RCP8.5). Besides, the INC strategy increased the share of broadleaved species and resulted in a synergy with recreation and timber production, and a trade-off with carbon sequestration and protection function. Our approach emphasized the disproportionally higher disturbance predisposition under the RCP8.5 climate change scenario, which may threaten currently unaffected mountain forests. Decision support systems accounting for climate change, disturbance predisposition, and BES can help coping with such complex planning situations.

SUPPLEMENTARY INFORMATION

The online version contains supplementary material available at 10.1007/s10113-022-02015-w.

Multiscale predictors of small tree survival across a heterogeneous tropical landscape

Uncertainties about controls on tree mortality make forest responses to land-use and climate change difficult to predict. We tracked biomass of tree functional groups in tropical forest inventories across Puerto Rico and the U.S. Virgin Islands, and with random forests we ranked 86 potential predictors of small tree survival (young or mature stems 2.5-12.6 cm diameter at breast height). Forests span dry to cloud forests, range in age, geology and past land use and experienced severe drought and storms. When excluding species as a predictor, top predictors are tree crown ratio and height, two to three species traits and stand to regional factors reflecting local disturbance and the system state (widespread recovery, drought, hurricanes). Native species, and species with denser wood, taller maximum height, or medium typical height survive longer, but short trees and species survive hurricanes better. Trees survive longer in older stands and with less disturbed canopies, harsher geoclimates (dry, edaphically dry, e.g., serpentine substrates, and highest-elevation cloud forest), or in intervals removed from hurricanes. Satellite image phenology and bands, even from past decades, are top predictors, being sensitive to vegetation type and disturbance. Covariation between stand-level species traits and geoclimate, disturbance and neighboring species types may explain why most neighbor variables, including introduced vs. native species, had low or no importance, despite univariate correlations with survival. As forests recovered from a hurricane in 1998 and earlier deforestation, small trees of introduced species, which on average have lighter wood, died at twice the rate of natives. After hurricanes in 2017, the total biomass of trees ≥12.7 cm dbh of the introduced species Spathodea campanulata spiked, suggesting that more frequent hurricanes might perpetuate this light-wooded species commonness. If hurricane recovery favors light-wooded species while drought favors others, climate change influences on forest composition and ecosystem services may depend on the frequency and severity of extreme climate events.

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.

The evolution, complexity and diversity of models of long-term forest dynamics

To assess the impacts of climate change on vegetation from stand to global scales, models of forest dynamics that include tree demography are needed. Such models are now available for 50 years, but the currently existing diversity of model formulations and its evolution over time are poorly documented. This hampers systematic assessments of structural uncertainties in model-based studies.We conducted a meta-analysis of 28 models, focusing on models that were used in the past five years for climate change studies. We defined 52 model attributes in five groups (basic assumptions, growth, regeneration, mortality and soil moisture) and characterized each model according to these attributes. Analyses of model complexity and diversity included hierarchical cluster analysis and redundancy analysis.Model complexity evolved considerably over the past 50 years. Increases in complexity were largest for growth processes, while complexity of modelled establishment processes increased only moderately. Model diversity was lowest at the global scale, and highest at the landscape scale. We identified five distinct clusters of models, ranging from very simple models to models where specific attribute groups are rendered in a complex manner and models that feature high complexity across all attributes.Most models in use today are not balanced in the level of complexity with which they represent different processes. This is the result of different model purposes, but also reflects legacies in model code, modelers' preferences, and the 'prevailing spirit of the epoch'. The lack of firm theories, laws and 'first principles' in ecology provides high degrees of freedom in model development, but also results in high responsibilities for model developers and the need for rigorous model evaluation. Synthesis. The currently available model diversity is beneficial: convergence in simulations of structurally different models indicates robust projections, while convergence of similar models may convey a false sense of certainty. The existing model diversity-with the exception of global models-can be exploited for improved projections based on multiple models. We strongly recommend balanced further developments of forest models that should particularly focus on establishment and mortality processes, in order to provide robust information for decisions in ecosystem management and policymaking.

Interaction of drought- and pathogen-induced mortality in Norway spruce and Scots pine

Pathogenic diseases frequently occur in drought-stressed trees. However, their contribution to the process of drought-induced mortality is poorly understood. We combined drought and stem inoculation treatments to study the physiological processes leading to drought-induced mortality in Norway spruce (Picea abies) and Scots pine (Pinus sylvestris) saplings infected with Heterobasidion annosum s.s. We analysed the saplings' water status, gas exchange, nonstructural carbohydrates (NSCs) and defence responses, and how they related to mortality. Saplings were followed for two growing seasons, including an artificially induced 3-month dormancy period. The combined drought and pathogen treatment significantly increased spruce mortality; however, no interaction between these stressors was observed in pine, although individually each stressor caused mortality. Our results suggest that pathogen infection decreased carbon reserves in spruce, reducing the capacity of saplings to cope with drought, resulting in increased mortality rates. Defoliation, relative water content and the starch concentration of needles were predictors of mortality in both species under drought and pathogen infection. Infection and drought stress create conflicting needs for carbon to compartmentalize the pathogen and to avoid turgor loss, respectively. Heterobasidion annosum reduces the functional sapwood area and shifts NSC allocation patterns, reducing the capacity of trees to cope with drought.

Reservoir water quality deterioration due to deforestation emphasizes the indirect effects of global change

Deforestation is currently a widespread phenomenon and a growing environmental concern in the era of rapid climate change. In temperate regions, it is challenging to quantify the impacts of deforestation on the catchment dynamics and downstream aquatic ecosystems such as reservoirs and disentangle these from direct climate change impacts, let alone project future changes to inform management. Here, we tackled this issue by investigating a unique catchment-reservoir system with two reservoirs in distinct trophic states (meso‑ and eutrophic), both of which drain into the largest drinking water reservoir in Germany. Due to the prolonged droughts in 2015-2018, the catchment of the mesotrophic reservoir lost an unprecedented area of forest (exponential increase since 2015 and ca. 17.1% loss in 2020 alone). We coupled catchment nutrient exports (HYPE) and reservoir ecosystem dynamics (GOTM-WET) models using a process-based modeling approach. The coupled model was validated with datasets spanning periods of rapid deforestation, which makes our future projections highly robust. Results show that in a short-term time scale (by 2035), increasing nutrient flux from the catchment due to vast deforestation (80% loss) can turn the mesotrophic reservoir into a eutrophic state as its counterpart. Our results emphasize the more prominent impacts of deforestation than the direct impact of climate warming in impairment of water quality and ecological services to downstream aquatic ecosystems. Therefore, we propose to evaluate the impact of climate change on temperate reservoirs by incorporating a time scale-dependent context, highlighting the indirect impact of deforestation in the short-term scale. In the long-term scale (e.g. to 2100), a guiding hypothesis for future research may be that indirect effects (e.g., as mediated by catchment dynamics) are as important as the direct effects of climate warming on aquatic ecosystems.

Future climate risks from stress, insects and fire across US forests

Forests are currently a substantial carbon sink globally. Many climate change mitigation strategies leverage forest preservation and expansion, but rely on forests storing carbon for decades to centuries. Yet climate‐driven disturbances pose critical risks to the long‐term stability of forest carbon. We quantify the climate drivers that influence wildfire and climate stress‐driven tree mortality, including a separate insect‐driven tree mortality, for the contiguous United States for current (1984–2018) and project these future disturbance risks over the 21st century. We find that current risks are widespread and projected to increase across different emissions scenarios by a factor of >4 for fire and >1.3 for climate‐stress mortality. These forest disturbance risks highlight pervasive climate‐sensitive disturbance impacts on US forests and raise questions about the risk management approach taken by forest carbon offset policies. Our results provide US‐wide risk maps of key climate‐sensitive disturbances for improving carbon cycle modeling, conservation and climate policy.

European Forest Governance: Status Quo and Optimising Options with Regard to the Paris Climate Target

This article assesses and develops policy instruments for forest governance in the EU. Methodologically, it examines opportunities and limits for negative emissions by means of a literature review. On this basis, it conducts a qualitative governance analysis of the most important instruments of EU forest policy and presents optimizing policy options, measured against the binding climate and biodiversity targets under international law. Our analysis shows that the potential benefits of afforestation and reforestation for climate mitigation are overestimated, and are often presented as the new saviours to assist in reaching climate neutrality, inter alia, since only biodiverse and thus resilient forests can function as a carbon sink in the long term. Furthermore, we demonstrate that the existing EU law fails to comply with climate and biodiversity targets. Quantity governance systems for livestock farming, fossil fuels and similar drivers of deforestation represent a more promising approach to forest governance than the dominant regulatory and subsidy-based governance. They are most effective when not directly addressing forests due to their heterogeneity but central damaging factors such as fossil fuels and livestock farming. Selected aspects of regulatory and subsidy law can supplement these quantity governance systems when focusing on certain easily attainable and thus controllable subjects. These include, e.g., the regulatory protection of old-growth forests with almost no exceptions and a complete conversion of all agricultural and forest subsidies to “public money for public services” to promote nature conservation and afforestation.

Unlocking Drought-Induced Tree Mortality: Physiological Mechanisms to Modeling

Drought-related tree mortality has become a major concern worldwide due to its pronounced negative impacts on the functioning and sustainability of forest ecosystems. However, our ability to identify the species that are most vulnerable to drought, and to pinpoint the spatial and temporal patterns of mortality events, is still limited. Model is useful tools to capture the dynamics of vegetation at spatiotemporal scales, yet contemporary land surface models (LSMs) are often incapable of predicting the response of vegetation to environmental perturbations with sufficient accuracy, especially under stressful conditions such as drought. Significant progress has been made regarding the physiological mechanisms underpinning plant drought response in the past decade, and plant hydraulic dysfunction has emerged as a key determinant for tree death due to water shortage. The identification of pivotal physiological events and relevant plant traits may facilitate forecasting tree mortality through a mechanistic approach, with improved precision. In this review, we (1) summarize current understanding of physiological mechanisms leading to tree death, (2) describe the functionality of key hydraulic traits that are involved in the process of hydraulic dysfunction, and (3) outline their roles in improving the representation of hydraulic function in LSMs. We urge potential future research on detailed hydraulic processes under drought, pinpointing corresponding functional traits, as well as understanding traits variation across and within species, for a better representation of drought-induced tree mortality in models.

Integrating plant physiology and community ecology across scales through trait-based models to predict drought mortality

Forests are a critical carbon sink and widespread tree mortality resulting from climate-induced drought stress has the potential to alter forests from a carbon sink to a source, causing a positive feedback on climate change. Process-based vegetation models aim to represent the current understanding of the underlying mechanisms governing plant physiological and ecological responses to climate. Yet model accuracy varies across scales, and regional-scale model predictive skill is frequently poor when compared with observations of drought-driven mortality. I propose a framework that leverages differences in model predictive skill across spatial scales, mismatches between model predictions and observations, and differences in the mechanisms included and absent across models to advance the understanding of the physiological and ecological processes driving observed patterns drought-driven mortality.

Forest Dynamics Models for Conservation, Restoration, and Management of Small Forests

Globally, there are myriad situations in which people aim to conserve, restore, or manage forest ecosystems at small spatial scales of 50 ha or less. To inform management, forest dynamics models provide an increasingly diverse and valuable portfolio of tools for projecting forest change under different management and environmental conditions. Yet, many models may not be appropriate or feasible to use in small forest management because of their design for larger-scale applications, the information needed to initialize models, or discrepancies between model outputs and information relevant for small forest management objectives. This review explores the suitability of 54 existing forest dynamics models to inform the management of small forests. We evaluated the characteristics of each model using five criteria with implications for small forest management: spatial resolution, number of species the model can simulate, inclusion of spatial structure, modeling approach, and mechanistic detail. While numerous models can be suitable under certain conditions, the review criteria led us to conclude that two models offered the broadest versatility and usability for small forest contexts, SORTIE and FORMIND. This review can help orient and guide small forest managers who wish to add modeling to their forest management efforts.

Reduced ecosystem resilience quantifies fine-scale heterogeneity in tropical forest mortality responses to drought

Sensitivity of forest mortality to drought in carbon-dense tropical forests remains fraught with uncertainty, while extreme droughts are predicted to be more frequent and intense. Here, the potential of temporal autocorrelation of high-frequency variability in Landsat Enhanced Vegetation Index (EVI), an indicator of ecosystem resilience, to predict spatial and temporal variations of forest biomass mortality is evaluated against in situ census observations for 64 site-year combinations in Costa Rican tropical dry forests during the 2015 ENSO drought. Temporal autocorrelation, within the optimal moving window of 24 months, demonstrated robust predictive power for in situ mortality (leave-one-out cross-validation R²  = 0.54), which allows for estimates of annual biomass mortality patterns at 30 m resolution. Subsequent spatial analysis showed substantial fine-scale heterogeneity of forest mortality patterns, largely driven by drought intensity and ecosystem properties related to plant water use such as forest deciduousness and topography. Highly deciduous forest patches demonstrated much lower mortality sensitivity to drought stress than less deciduous forest patches after elevation was controlled. Our results highlight the potential of high-resolution remote sensing to "fingerprint" forest mortality and the significant role of ecosystem heterogeneity in forest biomass resistance to drought.

An evaluation of multi-species empirical tree mortality algorithms for dynamic vegetation modelling

Tree mortality is key for projecting forest dynamics, but difficult to portray in dynamic vegetation models (DVMs). Empirical mortality algorithms (MAs) are often considered promising, but little is known about DVM robustness when employing MAs of various structures and origins for multiple species. We analysed empirical MAs for a suite of European tree species within a consistent DVM framework under present and future climates in two climatically different study areas in Switzerland and evaluated their performance using empirical data from old-growth forests across Europe. DVM projections under present climate showed substantial variations when using alternative empirical MAs for the same species. Under climate change, DVM projections showed partly contrasting mortality responses for the same species. These opposing patterns were associated with MA structures (i.e. explanatory variables) and occurred independent of species ecological characteristics. When comparing simulated forest structure with data from old-growth forests, we found frequent overestimations of basal area, which can lead to flawed projections of carbon sequestration and other ecosystem services. While using empirical MAs in DVMs may appear promising, our results emphasize the importance of selecting them cautiously. We therefore synthesize our insights into a guideline for the appropriate use of empirical MAs in DVM applications.

Leaf Shedding and Non-Stomatal Limitations of Photosynthesis Mitigate Hydraulic Conductance Losses in Scots Pine Saplings During Severe Drought Stress

During drought, trees reduce water loss and hydraulic failure by closing their stomata, which also limits photosynthesis. Under severe drought stress, other acclimation mechanisms are trigged to further reduce transpiration to prevent irreversible conductance loss. Here, we investigate two of them: the reversible impacts on the photosynthetic apparatus, lumped as non-stomatal limitations (NSL) of photosynthesis, and the irreversible effect of premature leaf shedding. We integrate NSL and leaf shedding with a state-of-the-art tree hydraulic simulation model (SOX+) and parameterize them with example field measurements to demonstrate the stress-mitigating impact of these processes. We measured xylem vulnerability, transpiration, and leaf litter fall dynamics in Pinus sylvestris (L.) saplings grown for 54 days under severe dry-down. The observations showed that, once transpiration stopped, the rate of leaf shedding strongly increased until about 30% of leaf area was lost on average. We trained the SOX+ model with the observations and simulated changes in root-to-canopy conductance with and without including NSL and leaf shedding. Accounting for NSL improved model representation of transpiration, while model projections about root-to-canopy conductance loss were reduced by an overall 6%. Together, NSL and observed leaf shedding reduced projected losses in conductance by about 13%. In summary, the results highlight the importance of other than purely stomatal conductance-driven adjustments of drought resistance in Scots pine. Accounting for acclimation responses to drought, such as morphological (leaf shedding) and physiological (NSL) adjustments, has the potential to improve tree hydraulic simulation models, particularly when applied in predicting drought-induced tree mortality.

Interspecific variation in mortality and growth and changes in their relationship with size class in an old-growth temperate forest

Understanding trade-offs between demographic parameters is crucial when investigating community assembly rules in high-diversity forests. To this end, we estimated mortality and growth parameters, and correlations among them, across entire size classes for 17 tree species (Betula, Carpinus, Fagus, Quercus, Castanea, Acer, Cerasus, Swida, Kalopanax, and Styrax) using a dataset over 18 years obtained from an old-growth forest in Japan.Size classes were represented by 12 categories determined by age, height, and diameter at breast height (DBH) from new seedlings to stems of DBH >85 cm. We derived the annual mortality and growth for each species and class using estimates of transition probabilities between classes. Trade-offs or synergies in growth and survival among species per size class were analyzed with and without the inclusion of phylogenetic relationships.Annual mortality showed U-shaped patterns across size classes for species that could potentially reach a DBH ≥55 cm: 0.2-0.98 for seedlings, 0.002-0.01 at DBH 35-45 cm, and ca. 0.01 at DBH ≥55 cm. Other species demonstrated monotonically decreasing mortality toward specific maximum size classes. When phylogenetic information was included in analyses, the correlations between survival and growth changed across size classes were significant for some classes: As an overall tendency, synergy was observed in growth and survival for seedling to sapling classes, trade-offs for juvenile to DBH 15-25 cm classes, and synergy again for larger classes. When phylogenetic information was not included, a significant trade-off was observed only at DBH 5-15 cm. Synthesis. Trade-offs at intermediate classes imply differentiation in demographic characteristics related to life history strategies. However, evolutionarily obtained demographic characteristics are not substantial drivers of niche differentiation in the study area. The polylemma of mortality, growth, and other parameters such as the onset of reproduction may also be important factors driving species-specific demographic traits.

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 (~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 Third 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: (1) climate change scenarios, (2) local site conditions, and (3) 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.

Increasing aridity will not offset CO2 fertilization in fast-growing eucalypts with access to deep soil water

Rising atmospheric [CO₂ ] (C_a ) generally enhances tree growth if nutrients are not limiting. However, reduced water availability and elevated evaporative demand may offset such fertilization. Trees with access to deep soil water may be able to mitigate such stresses and respond more positively to C_a . Here, we sought to evaluate how increased vapor pressure deficit and reduced precipitation are likely to modify the impact of elevated C_a (eC_a ) on tree productivity in an Australian Eucalyptus saligna Sm. plantation with access to deep soil water. We parameterized a forest growth simulation model (GOTILWA+) using data from two field experiments on E. saligna: a 2-year whole-tree chamber experiment with factorial C_a (ambient =380, elevated =620 μmol mol^-1 ) and watering treatments, and a 10-year stand-scale irrigation experiment. Model evaluation showed that GOTILWA+ can capture the responses of canopy C uptake to (1) rising vapor pressure deficit (D) under both C_a treatments; (2) alterations in tree water uptake from shallow and deep soil layers during soil dry-down; and (3) the impact of irrigation on tree growth. Simulations suggest that increasing C_a up to 700 μmol mol^-1 alone would result in a 33% increase in annual gross primary production (GPP) and a 62% increase in biomass over 10 years. However, a combined 48% increase in D and a 20% reduction in precipitation would halve these values. Our simulations identify high D conditions as a key limiting factor for GPP. They also suggest that rising C_a will compensate for increasing aridity limitations in E. saligna trees with access to deep soil water under non-nutrient limiting conditions, thereby reducing the negative impacts of global warming upon this eucalypt species. Simulation models not accounting for water sources available to deep-rooting trees are likely to overestimate aridity impacts on forest productivity and C stocks.

Assessing model performance via the most limiting environmental driver in two differently stressed pine stands

Climate change will impact forest productivity worldwide. Forecasting the magnitude of such impact, with multiple environmental stressors changing simultaneously, is only possible with the help of process-based models. In order to assess their performance, such models require careful evaluation against measurements. However, direct comparison of model outputs against observational data is often not reliable, as models may provide the right answers due to the wrong reasons. This would severely hinder forecasting abilities under unprecedented climate conditions. Here, we present a methodology for model assessment, which supplements the traditional output-to-observation model validation. It evaluates model performance through its ability to reproduce observed seasonal changes of the most limiting environmental driver (MLED) for a given process, here daily gross primary productivity (GPP). We analyzed seasonal changes of the MLED for GPP in two contrasting pine forests, the Mediterranean Pinus halepensis Mill. Yatir (Israel) and the boreal Pinus sylvestris L. Hyytiälä (Finland) from three years of eddy-covariance flux data. Then, we simulated the same period with a state-of-the-art process-based simulation model (LandscapeDNDC). Finally, we assessed if the model was able to reproduce both GPP observations and MLED seasonality. We found that the model reproduced the seasonality of GPP in both stands, but it was slightly overestimated without site-specific fine-tuning. Interestingly, although LandscapeDNDC properly captured the main MLED in Hyytiälä (temperature) and in Yatir (soil water availability), it failed to reproduce high-temperature and high-vapor pressure limitations of GPP in Yatir during spring and summer. We deduced that the most likely reason for this divergence is an incomplete description of stomatal behavior. In summary, this study validates the MLED approach as a model evaluation tool, and opens up new possibilities for model improvement.

Tackling unresolved questions in forest ecology: The past and future role of simulation models

Understanding the processes that shape forest functioning, structure, and diversity remains challenging, although data on forest systems are being collected at a rapid pace and across scales. Forest models have a long history in bridging data with ecological knowledge and can simulate forest dynamics over spatio-temporal scales unreachable by most empirical investigations.We describe the development that different forest modelling communities have followed to underpin the leverage that simulation models offer for advancing our understanding of forest ecosystems.Using three widely applied but contrasting approaches - species distribution models, individual-based forest models, and dynamic global vegetation models - as examples, we show how scientific and technical advances have led models to transgress their initial objectives and limitations. We provide an overview of recent model applications on current important ecological topics and pinpoint ten key questions that could, and should, be tackled with forest models in the next decade.Synthesis. This overview shows that forest models, due to their complementarity and mutual enrichment, represent an invaluable toolkit to address a wide range of fundamental and applied ecological questions, hence fostering a deeper understanding of forest dynamics in the context of global change.

Implications of size-dependent tree mortality for tropical forest carbon dynamics

Tropical forests are mitigating the ongoing climate crisis by absorbing more atmospheric carbon than they emit. However, widespread increases in tree mortality rates are decreasing the ability of tropical forests to assimilate and store carbon. A relatively small number of large trees dominate the contributions of these forests to the global carbon budget, yet we know remarkably little about how these large trees die. Here, we propose a cohesive and empirically informed framework for understanding and investigating size-dependent drivers of tree mortality. This theory-based framework enables us to posit that abiotic drivers of tree mortality-particularly drought, wind and lightning-regulate tropical forest carbon cycling via their disproportionate effects on large trees. As global change is predicted to increase the pressure from abiotic drivers, the associated deaths of large trees could rapidly and lastingly reduce tropical forest biomass stocks. Focused investigations of large tree death are needed to understand how shifting drivers of mortality are restructuring carbon cycling in tropical forests.

Why is Tree Drought Mortality so Hard to Predict?

Widespread tree mortality following droughts has emerged as an environmentally and economically devastating 'ecological surprise'. It is well established that tree physiology is important in understanding drought-driven mortality; however, the accuracy of predictions based on physiology alone has been limited. We propose that complicating factors at two levels stymie predictions of drought-driven mortality: (i) organismal-level physiological and site factors that obscure understanding of drought exposure and vulnerability and (ii) community-level ecological interactions, particularly with biotic agents whose effects on tree mortality may reverse expectations based on stress physiology. We conclude with a path forward that emphasizes the need for an integrative approach to stress physiology and biotic agent dynamics when assessing forest risk to drought-driven morality in a changing climate.

Large-scale variations in the dynamics of Amazon forest canopy gaps from airborne lidar data and opportunities for tree mortality estimates

We report large-scale estimates of Amazonian gap dynamics using a novel approach with large datasets of airborne light detection and ranging (lidar), including five multi-temporal and 610 single-date lidar datasets. Specifically, we (1) compared the fixed height and relative height methods for gap delineation and established a relationship between static and dynamic gaps (newly created gaps); (2) explored potential environmental/climate drivers explaining gap occurrence using generalized linear models; and (3) cross-related our findings to mortality estimates from 181 field plots. Our findings suggest that static gaps are significantly correlated to dynamic gaps and can inform about structural changes in the forest canopy. Moreover, the relative height outperformed the fixed height method for gap delineation. Well-defined and consistent spatial patterns of dynamic gaps were found over the Amazon, while also revealing the dynamics of areas never sampled in the field. The predominant pattern indicates 20-35% higher gap dynamics at the west and southeast than at the central-east and north. These estimates were notably consistent with field mortality patterns, but they showed 60% lower magnitude likely due to the predominant detection of the broken/uprooted mode of death. While topographic predictors did not explain gap occurrence, the water deficit, soil fertility, forest flooding and degradation were key drivers of gap variability at the regional scale. These findings highlight the importance of lidar in providing opportunities for large-scale gap dynamics and tree mortality monitoring over the Amazon.

Few generalizable patterns of tree-level mortality during extreme drought and concurrent bark beetle outbreaks

Tree mortality associated with drought and concurrent bark beetle outbreaks is expected to increase with further climate change. When these two types of disturbance occur in concert it complicates our ability to accurately predict future forest mortality. The recent extreme California USA drought and bark beetle outbreaks resulted in extensive tree mortality and provides a unique opportunity to examine questions of why some trees die while others survive these co-occurring disturbances. We use plot-level data combined with a three-proxy tree-level approach using radial growth, carbon isotopes, and resin duct metrics to evaluate 1) whether variability in stand structure, tree growth or size, carbon isotope discrimination, or defenses precede mortality, 2) how relationships between these proxies differ for surviving and now-dead trees, and 3) whether generalizable risk factors for tree mortality exist across pinyon pine (Pinus monophylla), ponderosa pine (P. ponderosa), white fir (Abies concolor), and incense cedar (Calocedrus decurrens) affected by the combination of drought and beetle outbreaks. We find that risk factors associated with mortality differ between species, and that few generalizable patterns exist when bark beetle outbreaks occur in concert with a particularly long, hot drought. We see evidence that both long-term differences in physiology and shorter-term beetle-related selection and variability in defenses influence mortality susceptibility for ponderosa pine, whereas beetle dynamics may play a more prominent role in mortality patterns for white fir and pinyon pine. In contrast, incense cedar mortality appears to be attributable to long-term effects of growth suppression. Risk factors that predispose some trees to drought and beetle-related mortality likely reflect species-specific strategies for dealing with these particular disturbance types. The combined influence of beetles and drought necessitates the consideration of multiple, species-specific risk factors to more accurately model forest mortality in the face of similar extreme events more likely under future climates.

Simulating forest resilience: A review

AIM

Simulation models are important tools for quantifying the resilience (i.e., persistence under changed environmental conditions) of forest ecosystems to global change. We synthesized the modelling literature on forest resilience, summarizing common models and applications in resilience research, and scrutinizing the implementation of important resilience mechanisms in these models. Models applied to assess resilience are highly diverse, and our goal was to assess how well they account for important resilience mechanisms identified in experimental and empirical research.

LOCATION

Global.

TIME PERIOD

1994 to 2019.

MAJOR TAXA STUDIED

Trees.

METHODS

We reviewed the forest resilience literature using online databases, selecting 119 simulation modelling studies for further analysis. We identified a set of resilience mechanisms from the general resilience literature and analysed models for their representation of these mechanisms. Analyses were grouped by investigated drivers (resilience to what) and responses (resilience of what), as well as by the type of model being used.

RESULTS

Models used to study forest resilience varied widely, from analytical approaches to complex landscape simulators. The most commonly addressed questions were associated with resilience of forest cover to fire. Important resilience mechanisms pertaining to regeneration, soil processes, and disturbance legacies were explicitly simulated in only 34 to 46% of the model applications.

MAIN CONCLUSIONS

We found a large gap between processes identified as underpinning forest resilience in the theoretical and empirical literature, and those represented in models used to assess forest resilience. Contemporary forest models developed for other goals may be poorly suited for studying forest resilience during an era of accelerating change. Our results highlight the need for a new wave of model development to enhance understanding of and management for resilient forests.

Gains or Losses in Forest Productivity under Climate Change? The Uncertainty of CO2 Fertilization and Climate Effects

Global warming poses great challenges for forest managers regarding adaptation strategies and species choices. More frequent drought events and heat spells are expected to reduce growth and increase mortality. Extended growing seasons, warming and elevated CO2 (eCO2) can also positively affect forest productivity. We studied the growth, productivity and mortality of beech (Fagus sylvatica L.) and fir (Abies alba Mill.) in the Black Forest (Germany) under three climate change scenarios (representative concentration pathways (RCP): RCP2.6, RCP4.5, RCP8.5) using the detailed biogeochemical forest growth model GOTILWA+. Averaged over the entire simulation period, both species showed productivity losses in RCP2.6 (16–20%) and in RCP4.5 (6%), but productivity gains in RCP8.5 (11–17%). However, all three scenarios had a tipping point (between 2035–2060) when initial gains in net primary productivity (NPP) (6–29%) eventually turned into losses (1–26%). With eCO2 switched off, the losses in NPP were 26–51% in RCP2.6, 36–45% in RCP4.5 and 33–71% in RCP8.5. Improved water-use efficiency dampened drought effects on NPP between 4 and 5%. Tree mortality increased, but without notably affecting forest productivity. Concluding, cultivation of beech and fir may still be possible in the study region, although severe productivity losses can be expected in the coming decades, which will strongly depend on the dampening CO2 fertilization effect.

Forest responses to simulated elevated CO2 under alternate hypotheses of size- and age-dependent mortality

Elevated atmospheric carbon dioxide (eCO₂ ) is predicted to increase growth rates of forest trees. The extent to which increased growth translates to changes in biomass is dependent on the turnover time of the carbon, and thus tree mortality rates. Size- or age-dependent mortality combined with increased growth rates could result in either decreased carbon turnover from a speeding up of tree life cycles, or increased biomass from trees reaching larger sizes, respectively. However, most vegetation models currently lack any representation of size- or age-dependent mortality and the effect of eCO₂ on changes in biomass and carbon turnover times is thus a major source of uncertainty in predictions of future vegetation dynamics. Using a reduced-complexity form of the vegetation demographic model the Functionally Assembled Terrestrial Ecosystem Simulator to simulate an idealised tropical forest, we find increases in biomass despite reductions in carbon turnover time in both size- and age-dependent mortality scenarios in response to a hypothetical eCO₂ -driven 25% increase in woody net primary productivity (wNPP). Carbon turnover times decreased by 9.6% in size-dependent mortality scenarios due to a speeding up of tree life cycles, but also by 2.0% when mortality was age-dependent, as larger crowns led to increased light competition. Increases in aboveground biomass (AGB) were much larger when mortality was age-dependent (24.3%) compared with size-dependent (13.4%) as trees reached larger sizes before death. In simulations with a constant background mortality rate, carbon turnover time decreased by 2.1% and AGB increased by 24.0%, however, absolute values of AGB and carbon turnover were higher than in either size- or age-dependent mortality scenario. The extent to which AGB increases and carbon turnover decreases will thus depend on the mechanisms of large tree mortality: if increased size itself results in elevated mortality rates, then this could reduce by about half the increase in AGB relative to the increase in wNPP.

Hundred year projected carbon loads and species compositions for four National Forests in the northwestern USA

BACKGROUND

Forests are an important component of the global carbon balance, and climate sensitive growth and yield models are an essential tool when predicting future forest conditions. In this study, we used the dynamic climate capability of the Forest Vegetation Simulator (FVS) to simulate future (100 year) forest conditions on four National Forests in the northwestern USA: Payette National Forest (NF), Ochoco NF, Gifford Pinchot NF, and Siuslaw NF. Using Forest Inventory and Analysis field plots, aboveground carbon estimates and species compositions were simulated with Climate-FVS for the period between 2016 and 2116 under a no climate change scenario and a future climate scenario. We included a sensitivity analysis that varied calculated disturbance probabilities and the dClim rule, which is one method used by Climate-FVS to introduce climate-related mortality. The dClim rule initiates mortality when the predicted climate change at a site is greater than the change in climate associated with a predetermined shift in elevation.

RESULTS

Results of the simulations indicated the dClim rule influenced future carbon projections more than estimates of disturbance probability. Future aboveground carbon estimates increased and species composition remained stable under the no climate change scenario. The future climate scenario we tested resulted in less carbon at the end of the projections compared to the no climate change scenarios for all cases except when the dClim rule was disengaged on the Payette NF. Under the climate change scenario, species compositions shifted to climatically adapted species or early successional species.

CONCLUSION

This research highlights the need to consider climate projections in long-term planning or future forest conditions may be unexpected. Forest managers and planners could perform similar simulations and use the results as a planning tool when analyzing climate change effects at the National Forest level.

Tree defence and bark beetles in a drying world: carbon partitioning, functioning and modelling

Drought has promoted large-scale, insect-induced tree mortality in recent years, with severe consequences for ecosystem function, atmospheric processes, sustainable resources and global biogeochemical cycles. However, the physiological linkages among drought, tree defences, and insect outbreaks are still uncertain, hindering our ability to accurately predict tree mortality under on-going climate change. Here we propose an interdisciplinary research agenda for addressing these crucial knowledge gaps. Our framework includes field manipulations, laboratory experiments, and modelling of insect and vegetation dynamics, and focuses on how drought affects interactions between conifer trees and bark beetles. We build upon existing theory and examine several key assumptions: (1) there is a trade-off in tree carbon investment between primary and secondary metabolites (e.g. growth vs defence); (2) secondary metabolites are one of the main component of tree defence against bark beetles and associated microbes; and (3) implementing conifer-bark beetle interactions in current models improves predictions of forest disturbance in a changing climate. Our framework provides guidance for addressing a major shortcoming in current implementations of large-scale vegetation models, the under-representation of insect-induced tree mortality.

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

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.

Towards a New Generation of Trait-Flexible Vegetation Models

Plant trait variability, emerging from eco-evolutionary dynamics that range from alleles to macroecological scales, is one of the most elusive, but possibly most consequential, aspects of biodiversity. Plasticity, epigenetics, and genetic diversity are major determinants of how plants will respond to climate change, yet these processes are rarely represented in current vegetation models. Here, we provide an overview of the challenges associated with understanding the causes and consequences of plant trait variability, and review current developments to include plasticity and evolutionary mechanisms in vegetation models. We also present a roadmap of research priorities to develop a next generation of vegetation models with flexible traits. Including trait variability in vegetation models is necessary to better represent biosphere responses to global change.

CARACTERIZAÇÃO FLORÍSTICA, ESTRUTURAL E QUALITATIVA DE UM FRAGMENTO DE FLORESTA ESTACIONAL DECIDUAL, NA REGIÃO NORTE DO RIO GRANDE DO SUL

O presente trabalho teve por objetivo realizar a caracterização florística, estrutural e qualitativa de um fragmento de Floresta Estacional Decidual, na região do Alto Uruguai, em Frederico Westphalen-RS. Para a amostragem da vegetação foi instalada uma parcela permanente de 100 x 100 m, composta por 100 subunidades de 10 x 10 m. Todos os indivíduos com CAP≥31,4 foram mensurados e identificados em gênero, espécie e família botânica. A estrutura foi caracterizada quanto à densidade, dominância, frequência e índice de valor de importância. Na caracterização qualitativa, os fustes foram qualificados visualmente quanto à forma e à sanidade. No total foram mensurados 580 indivíduos e, dentre estes, 49 gêneros, 57 espécies, 28 famílias botânicas e apenas 1 espécie não identificada. Fabaceae foi a família que mais se destacou em número de gêneros (n=9), espécies (n=10) e indivíduos (n=146). As espécies Trichilia claussenii C. DC., Nectandra megapotamica (Spreng.) Mez e Holocalyx balansae Micheli, com valores de IVI de 14,0%, 9,1% e 7,3%, respectivamente, foram as mais importantes do fragmento florestal. Quanto à estrutura vertical, 95,2% dos indivíduos pertencem aos estratos inferior e médio. Em termos de números de indivíduos e dominância por espécie, destacaram-se T. claussenii no estrato inferior (26,8% e 21,5%), N. megapotamica no estrato médio (19,4% e 13%) e H. balansae no estrato superior (25,0% e 32,9%). O fragmento florestal caracteriza-se por apresentar indivíduos com fustes levemente tortuosos e saudáveis, sem a incidência de danos que possam impedir um eventual aproveitamento madeireiro.

A scalable model of vegetation transitions using deep neural networks

In times of rapid global change, anticipating vegetation changes and assessing their impacts is of key relevance to managers and policy makers. Yet, predicting vegetation dynamics often suffers from an inherent scale mismatch, with abundant data and process understanding being available at a fine spatial grain, but the relevance for decision-making is increasing with spatial extent.We present a novel approach for scaling vegetation dynamics (SVD), using deep learning to predict vegetation transitions. Vegetation is discretized into a large number (103-106) of potential states based on its structure, composition and functioning. Transition probabilities between states are estimated via a deep neural network (DNN) trained on observed or simulated vegetation transitions in combination with environmental variables. The impact of vegetation transitions on important ecological indicators is quantified by probabilistically linking attributes such as carbon storage and biodiversity to vegetation states.Here, we describe the SVD approach and present results of applying the framework in a meta-modelling context. We trained a DNN using simulations of a process-based forest landscape model for a complex mountain forest landscape under different climate scenarios. Subsequently, we evaluated the ability of SVD to project long-term vegetation dynamics and the resulting changes in forest carbon storage and biodiversity. SVD captured spatial (e.g. elevational gradients) and temporal (e.g. species succession) patterns of vegetation dynamics well, and responded realistically to changing environmental conditions. In addition, we tested the computational efficiency of the approach, highlighting the utility of SVD for country- to continental scale applications. SVD is the-to our knowledge-first vegetation model harnessing deep neural networks. The approach has high predictive accuracy and is able to generalize well beyond training data. SVD was designed to run on widely available input data (e.g. vegetation states defined from remote sensing, gridded global climate datasets) and exceeds the computational performance of currently available highly optimized landscape models by three to four orders of magnitude. We conclude that SVD is a promising approach for combining detailed process knowledge on fine-grained ecosystem processes with the increasingly available big ecological datasets for improved large-scale projections of vegetation dynamics.