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

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.

Feedback loops drive ecological succession: towards a unified conceptual framework

The core principle shared by most theories and models of succession is that, following a major disturbance, plant-environment feedback dynamics drive a directional change in the plant community. The most commonly studied feedback loops are those in which the regrowth of the plant community causes changes to the abiotic (e.g. soil nutrients) or biotic (e.g. dispersers) environment, which differentially affect species availability or performance. This, in turn, leads to shifts in the species composition of the plant community. However, there are many other PE feedback loops that potentially drive succession, each of which can be considered a model of succession. While plant-environment feedback loops in principle generate predictable successional trajectories, succession is generally observed to be highly variable. Factors contributing to this variability are the stochastic processes involved in feedback dynamics, such as individual mortality and seed dispersal, and extrinsic causes of succession, which are not affected by changes in the plant community but do affect species performance or availability. Both can lead to variation in the identity of dominant species within communities. This, in turn, leads to further contingencies if these species differ in their effect on their environment (priority effects). Predictability and variability are thus intrinsically linked features of ecological succession. We present a new conceptual framework of ecological succession that integrates the propositions discussed above. This framework defines seven general causes: landscape context, disturbance and land-use, biotic factors, abiotic factors, species availability, species performance, and the plant community. When involved in a feedback loop, these general causes drive succession and when not, they are extrinsic causes that create variability in successional trajectories and dynamics. The proposed framework provides a guide for linking these general causes into causal pathways that represent specific models of succession. Our framework represents a systematic approach to identifying the main feedback processes and causes of variation at different successional stages. It can be used for systematic comparisons among study sites and along environmental gradients, to conceptualise studies, and to guide the formulation of research questions and design of field studies. Mapping an extensive field study onto our conceptual framework revealed that the pathways representing the study's empirical outcomes and conceptual model had important differences, underlining the need to move beyond the conceptual models that currently dominate in specific fields and to find ways to examine the importance of and interactions among alternative causal pathways of succession. To further this aim, we argue for integrating long-term studies across environmental and anthropogenic gradients, combined with controlled experiments and dynamic modelling.

Modeling forest landscape futures: Full scale simulation of realistic socioeconomic scenarios in Estonia

For political and administrative governance of land-use decisions, high-resolution and reliable spatial models are required over large areas and for various time horizons. We present a process-centered simulation model 'NextStand' (a forest landscape model, FLM) and its R-script, which predicts regional forest characteristics at a forest stand resolution. The model uses whole area stand data and is optimized for realistic iterative timber harvesting decisions, based on stand compositions (developing over time) and locations. We used the model for simulating spatial predictions of the Estonian forests in North Europe (2.3 Mha, about 2 M stands); the decisions were parameterized by land ownership, protection regimes, and rules of clear-cut harvesting. We illustrate the model application as a potential broad-scale Decision Support Tool by predicting how the forest age composition, placement of clear-cut areas, and connectivity of old stands will develop until the year 2050 under future scenarios. The country-scale outputs had a generally low within-scenario variance, which enabled to estimate some main land-use effects and uncertainties at small computing efforts. In forestry terms, we show that a continuation of recent intensive forest management trends will produce a decline of the national timber supplies in Estonia, which greatly varies among ownership types. In a conservation perspective, the current level of 13% forest area strictly protected can maintain an overall area of old forests by 2050, but their isolation is a problem for biodiversity conservation. The behavior of low-intensity forest management units (owners) and strict governance of clear-cut harvesting rules emerged as key questions for regional forest sustainability. Our study confirms that high-resolution modeling of future spatial composition of forest land is feasible when one can (i) delineate predictable spatial units of transformation (including management) and (ii) capture their variability of temporal change with simple ecological and socioeconomic (including human decision-making) variables.

Unraveling the potential of environmental DNA for deciphering recent advances in plant-animal interactions: a systematic review

MAIN CONCLUSION

Environmental DNA-based monitoring provides critical insights for enhancing our understanding of plant-animal interactions in the context of worldwide biodiversity decrease for developing a global framework for effective plant biodiversity conservation. To understand the ecology and evolutionary patterns of plant-animal interactions (PAI) and their pivotal roles in ecosystem functioning advances in molecular ecology tools such as Environmental DNA (eDNA) provide unprecedented research avenues. These methods being non-destructive in comparison to traditional biodiversity monitoring methods, enhance the discernment of ecosystem health, integrity, and complex interactions. This review intends to offer a systematic and critical appraisal of the prospective of eDNA for investigating PAI. The review thoroughly discusses and analyzes the recent reports (2015-2022) employing preferred reporting items for systematic reviews and meta-analyses (PRISMA) to outline the recent progression in eDNA approaches for elucidating PAI. The current review envisages that eDNA has a significant potential to monitor both plants and associated cohort of prospective pollinators (avian species and flowering plants, bees and plants, arthropods and plants, bats and plants, etc.). Furthermore, a brief description of the factors that influence the utility and interpretation of PAI eDNA is also presented. The review establishes that factors such as biotic and abiotic, primer selection and taxonomic resolution, and indeterminate spatio-temporal scales impact the availability and longevity of eDNA. The study also identified the limitations that influence PAI detection and suggested possible solutions for better execution of these molecular approaches. Overcoming these research caveats will augment the assortment of PAI analysis through eDNA that could be vital for ecosystem health and integrity. This review forms a critical guide and offers prominent insights for ecologists, environmental managers and researchers to assess and evaluate plant-animal interaction through environmental DNA.

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.

Evolutionary adaptation of trees and modelled future larch forest extent in Siberia

With changing climate, the boreal forest could potentially migrate north and become threatened by droughts in the south. However, whether larches, the dominant tree species in eastern Siberia, can adapt to novel situations is largely unknown but is crucial for predicting future population dynamics. Exploring variable traits and trait adaptation through inheritance in an individual-based model can improve our understanding and help future projections. We updated the individual-based spatially explicit vegetation model LAVESI (Larix Vegetation Simulator), used for forest predictions in Eastern Siberia, with trait value variation and incorporated inheritance of parental values to their offspring. Forcing the model with both past and future climate projections, we simulated two areas - the expanding northern treeline and a southerly area experiencing drought. While the specific trait of 'seed weight' regulates migration, the abstract 'drought resistance' protects stands. We show that trait variation with inheritance leads to an increase in migration rate (∼ 3% area increase until 2100). The drought resistance simulations show that, under increasing stress, including adaptive traits leads to larger surviving populations (17% of threatened under RCP 4.5 (Representative Concentration Pathway)). We show that with the increase expected under the RCP 8.5 scenario vast areas (80% of the extrapolated area) of larch forest are threatened and could disappear due to drought as adaptation plays only a minor role under strong warming. We conclude that variable traits facilitate the availability of variants under environmental changes. Inheritance allows populations to adapt to environments and promote successful traits, which leads to populations that can spread faster and be more resilient, provided the changes are not too drastic in both time and magnitude. We show that trait variation and inheritance contribute to more accurate models that can improve our understanding of responses of boreal forests to global change.

Simulating diverse forest management options in a changing climate on a Pinus nigra subsp. laricio plantation in Southern Italy

Mediterranean pine plantations provide several ecosystem services but are vulnerable to climate change. Forest management might play a strategic role in the adaptation of Mediterranean forests, but the joint effect of climate change and diverse management options have seldom been investigated together. Here, we simulated the development of a Laricio pine (Pinus nigra subsp. laricio) stand in the Bonis watershed (southern Italy) from its establishment in 1958 up to 2095 using a state-of-the-science process-based forest model. The model was run under three climate scenarios corresponding to increasing levels of atmospheric CO₂ concentration and warming, and six management options with different goals, including wood production and renaturalization. We analysed the effect of climate change on annual carbon fluxes (i.e., gross and net primary production) and stocks (i.e., basal area, standing and harvested carbon woody stocks) of the autotrophic compartment, as well as the impact of different management options compared to a no management baseline. Results show that higher temperatures (+3 to +5 °C) and lower precipitation (-20 % to -22 %) will trigger a decrease in net primary productivity in the second half of the century. Compared to no management, the other options had a moderate effect on carbon fluxes over the whole simulation (between -14 % and +11 %). While standing woody biomass was reduced by thinning interventions and the shelterwood system (between -5 % and -41 %), overall carbon stocks including the harvested wood were maximized (between +41 % and +56 %). Results highlight that management exerts greater effects on the carbon budget of Laricio pine plantations than climate change alone, and that climate change and management are largely independent (i.e., no strong interaction effects). Therefore, appropriate silvicultural strategies might enhance potential carbon stocks and improve forest conditions, with cascading positive effects on the provision of ecosystem services in Mediterranean pine plantations.

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 Roles of the Moss Layer in Mediating Tree Seedling Environmental Stress, Mercury Exposure, and Regeneration in High-Elevation Conifer Forests

The persistence of future forests depends on the success of tree seedlings which are experiencing increasing physiological stress from changing climate and air pollution. Although the moss layer can serve as an important substrate for tree seedlings, its potential for reducing environmental stress and enhancing the establishment of seedlings remains poorly understood. We tested if the moss layer decreased environmental stress and increased the abundance of balsam fir seedlings dominant in high-elevation forests of northeastern United States that are sensitive to changing climate and mercury deposition. We surveyed balsam fir seedling density by substrate (moss, litter, other) on 120 quadrats (1 × 1 m) in two contrasting canopy environments (in gaps and under canopies), measured seedling stress, and quantified mercury content in seedlings and substrates. We observed that, in both canopy environments, tree seedlings established on moss exhibited (i) increased density, (ii) decreased physiological stress, and (iii) higher potential to recruit into larger size classes, compared to seedlings established in litter. Regardless of canopy environment, seedling foliar mercury levels did not correspond to substrate mercury despite large differences in substrate mercury concentrations (relative to moss, litter concentrations were ~ 4-times greater and soil concentrations were ~ 6-times greater), likely reflecting the dominance of foliar over root uptake of mercury. Because the moss layer appeared to mitigate seedling drought stress, and to increase seedling establishment and recruitment compared to other substrates, these microsite effects should be considered in models predicting forest regeneration and dynamics under increased drought stress associated with the ongoing climate warming.

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.

Strategic roadmap to assess forest vulnerability under air pollution and climate change

Although it is an integral part of global change, most of the research addressing the effects of climate change on forests have overlooked the role of environmental pollution. Similarly, most studies investigating the effects of air pollutants on forests have generally neglected the impacts of climate change. We review the current knowledge on combined air pollution and climate change effects on global forest ecosystems and identify several key research priorities as a roadmap for the future. Specifically, we recommend (1) the establishment of much denser array of monitoring sites, particularly in the South Hemisphere; (2) further integration of ground and satellite monitoring; (3) generation of flux-based standards and critical levels taking into account the sensitivity of dominant forest tree species; (4) long-term monitoring of N, S, P cycles and base cations deposition together at global scale; (5) intensification of experimental studies, addressing the combined effects of different abiotic factors on forests by assuring a better representation of taxonomic and functional diversity across the ~73,000 tree species on Earth; (6) more experimental focus on phenomics and genomics; (7) improved knowledge on key processes regulating the dynamics of radionuclides in forest systems; and (8) development of models integrating air pollution and climate change data from long-term monitoring programs.

A Proposal for a Forest Digital Twin Framework and Its Perspectives

The increasing importance of forest ecosystems for human society and planetary health is widely recognized, and the advancement of data collection technologies enables new and integrated ways for forest ecosystems monitoring. Therefore, the target of this paper is to propose a framework to design a forest digital twin (FDT) that, by integrating different state variables at both tree and forest levels, creates a virtual copy of the forest. The integration of these data sets could be used for scientific purposes, for reporting the health status of forests, and ultimately for implementing sustainable forest management practices on the basis of the use cases that a specific implementation of the framework would underpin. Achieving such outcomes requires the twinning of single trees as a core element of the FDT by recording the physical and biotic state variables of the tree and of the near environment via real–virtual digital sockets. Following a nested approach, the twinned trees and the related physical and physiological processes are then part of a broader twinning of the entire forest realized by capturing data at forest scale from sources such as remote sensing technologies and flux towers. Ultimately, to unlock the economic value of forest ecosystem services, the FDT should implement a distributed ledger-based on blockchain and smart contracts to ensure the highest transparency, reliability, and thoroughness of the data and the related transactions and to sharpen forest risk management with the final goal to improve the capital flow towards sustainable practices of forest management.

Towards a More Realistic Simulation of Plant Species with a Dynamic Vegetation Model Using Field-Measured Traits: The Atlas Cedar, a Case Study

Improving the model-based predictions of plant species under a projected climate is essential to better conserve our biodiversity. However, the mechanistic link between climatic variation and plant response at the species level remains relatively poorly understood and not accurately developed in Dynamic Vegetation Models (DVMs). We investigated the acclimation to climate of Cedrus atlantica (Atlas cedar), an endemic endangered species from northwestern African mountains, in order to improve the ability of a DVM to simulate tree growth under climatic gradients. Our results showed that the specific leaf area, leaf C:N and sapwood C:N vary across the range of the species in relation to climate. Using the model parameterized with the three traits varying with climate could improve the simulated local net primary productivity (NPP) when compared to the model parameterized with fixed traits. Quantifying the influence of climate on traits and including these variations in DVMs could help to better anticipate the consequences of climate change on species dynamics and distributions. Additionally, the simulation with computed traits showed dramatic drops in NPP over the course of the 21st century. This finding is in line with other studies suggesting the decline in the species in the Rif Mountains, owing to increasing water stress.

Simulating tree growth response to climate change in structurally diverse oak and beech forests

This study aimed to simulate oak and beech forest growth under various scenarios of climate change and to evaluate how the forest response depends on site properties and particularly on stand characteristics using the individual process-based model HETEROFOR. First, this model was evaluated on a wide range of site conditions. We used data from 36 long-term forest monitoring plots to initialize, calibrate, and evaluate HETEROFOR. This evaluation showed that HETEROFOR predicts individual tree radial growth and height increment reasonably well under different growing conditions when evaluated on independent sites. In our simulations under constant CO₂ concentration ([CO₂]_cst) for the 2071-2100 period, climate change induced a moderate net primary production (NPP) gain in continental and mountainous zones and no change in the oceanic zone. The NPP changes were negatively affected by air temperature during the vegetation period and by the annual rainfall decrease. To a lower extent, they were influenced by soil extractable water reserve and stand characteristics. These NPP changes were positively affected by longer vegetation periods and negatively by drought for beech and larger autotrophic respiration costs for oak. For both species, the NPP gain was much larger with rising CO₂ concentration ([CO₂]_var) mainly due to the CO₂ fertilisation effect. Even if the species composition and structure had a limited influence on the forest response to climate change, they explained a large part of the NPP variability (44% and 34% for [CO₂]_cst and [CO₂]_var, respectively) compared to the climate change scenario (5% and 29%) and the inter-annual climate variability (20% and 16%). This gives the forester the possibility to act on the productivity of broadleaved forests and prepare them for possible adverse effects of climate change by reinforcing their resilience.

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.