Towards a New Generation of Trait-Flexible Vegetation Models

Plant traits shape global spatiotemporal variations in photosynthetic efficiency

Photosynthetic efficiency (PE) quantifies the fraction of absorbed light used in photochemistry to produce chemical energy during photosynthesis and is essential for understanding ecosystem productivity and the global carbon cycle, particularly under conditions of vegetation stress. However, nearly 60% of the global spatiotemporal variance in terrestrial PE remains unexplained. Here we integrate remote sensing and eco-evolutionary optimality theory to derive key plant traits, alongside explainable machine learning and global eddy covariance observations, to uncover the drivers of daily PE variations. Incorporating plant traits into our model increases the explained daily PE variance from 36% to 80% for C3 vegetation and from 54% to 84% for C4 vegetation compared with using climate data alone. Key plant traits-including chlorophyll content, leaf longevity and leaf mass per area-consistently emerge as important factors across global biomes and temporal scales. Water availability and light conditions are also critical in regulating PE, underscoring the need for an integrative approach that combines plant traits with climatic factors. Overall, our findings demonstrate the potential of remote sensing and eco-evolutionary optimality theory to capture principal PE drivers, offering valuable tools for more accurately predicting ecosystem productivity and improving Earth system models under climate change.

Stomata Are Driving the Direction of CO2-Induced Water-Use Efficiency Gain in Selected Tropical Trees in Fiji

Understanding plant physiological response to a rising atmospheric CO2 concentration (ca) is key in predicting Earth system plant-climate feedbacks; however, the effects of long-term rising ca on plant gas-exchange characteristics in the tropics are largely unknown. Studying this long-term trend using herbarium records is challenging due to specimen trait variation. We assessed the impact of a ca rise of ~95 ppm (1927-2015) on the intrinsic water-use efficiency (iWUE) and maximum stomatal conductance (gsmax) of five tropical tree species in Fiji using the isotopic composition and stomatal traits of herbarium leaves. Empirical results were compared with simulated values using models that uniquely incorporated the variation in the empirical gsmax responses and species-specific parameterisation. The magnitude of the empirical iWUE and gsmax response was species-specific, ranging from strong to negligible. Stomatal density was more influential than the pore size in determining the gsmax response to ca. While our simulation results indicated that photosynthesis is the main factor contributing to the iWUE gain, stomata were driving the iWUE trend across the tree species. Generally, a stronger increase in the iWUE was accompanied by a stronger decline in stomatal response. This study demonstrates that the incorporation of variation in the gsmax in simulations is necessary for assessing an individual species' iWUE response to changing ca.

Environmental versus phylogenetic controls on leaf nitrogen and phosphorous concentrations in vascular plants

Global patterns of leaf nitrogen (N) and phosphorus (P) stoichiometry have been interpreted as reflecting phenotypic plasticity in response to the environment, or as an overriding effect of the distribution of species growing in their biogeochemical niches. Here, we balance these contrasting views. We compile a global dataset of 36,413 paired observations of leaf N and P concentrations, taxonomy and 45 environmental covariates, covering 7,549 sites and 3,700 species, to investigate how species identity and environmental variables control variations in mass-based leaf N and P concentrations, and the N:P ratio. We find within-species variation contributes around half of the total variation, with 29%, 31%, and 22% of leaf N, P, and N:P variation, respectively, explained by environmental variables. Within-species plasticity along environmental gradients varies across species and is highest for leaf N:P and lowest for leaf N. We identified effects of environmental variables on within-species variation using random forest models, whereas effects were largely missed by widely used linear mixed-effect models. Our analysis demonstrates a substantial influence of the environment in driving plastic responses of leaf N, P, and N:P within species, which challenges reports of a fixed biogeochemical niche and the overriding importance of species distributions in shaping global patterns of leaf N and P.

'How to adapt forests?'-Exploring the role of leaf trait diversity for long-term forest biomass under new climate normals

Forests, critical components of global ecosystems, face unprecedented challenges due to climate change. This study investigates the influence of functional diversity-as a component of biodiversity-to enhance long-term biomass of European forests in the context of changing climatic conditions. Using the next-generation flexible trait-based vegetation model, LPJmL-FIT, we explored the impact of functional diversity on long-term forest biomass under three different climate change scenarios (video abstract: https://www.pik-potsdam.de/~billing/video/2023/video_abstract_billing_et_al_LPJmLFIT.mp4). Four model set-ups were tested with varying degrees of functional diversity and best-suited functional traits. Our results show that functional diversity positively influences long-term forest biomass, particularly when climate warming is low (RCP2.6). Under these conditions, high-diversity simulations led to an approximately 18.2% increase in biomass compared to low-diversity experiments. However, as climate change intensity increased, the benefits of functional diversity diminished (RCP8.5). A Bayesian multilevel analysis revealed that both full leaf trait diversity and diversity of plant functional types contributed significantly to biomass enhancement under low warming scenarios in our model simulations. Under strong climate change, the presence of a mixture of different functional groups (e.g. summergreen and evergreen broad-leaved trees) was found more beneficial than the diversity of leaf traits within a functional group (e.g. broad-leaved summergreen trees). Ultimately, this research challenges the notion that planting only the most productive and climate-suited trees guarantees the highest future biomass and carbon sequestration. We underscore the importance of high functional diversity and the potential benefits of fostering a mixture of tree functional types to enhance long-term forest biomass in the face of climate change.

Warming drives feedback between plant phenotypes and ecosystem functioning in sub-Antarctic ponds

Ample evidence indicates that warming affects individuals in plant communities, ultimately threatening biodiversity. Individual plants in communities are also exposed to plant-plant interaction that may affect their performance. However, trait responses to these two constraints have usually been studied separately, while they may influence processes at the ecosystem level. In turn, these ecological modifications may impact the phenotypes of plants through nutrient availability and uptake. We developed an experimental approach based on the macrophyte communities in the ponds of the sub-Antarctic Iles Kerguelen. Individuals of the species Limosella australis were grown under different temperature × plant-plant interaction treatments to assess their trait responses and create litters with different characteristics. The litters were then decomposed in the presence of individual plants at different temperatures to examine effects on ecosystem functioning and potential feedback affecting plant trait values. Leaf resource-acquisition- and -conservation-related traits were altered in the context of temperature × plant-plant interaction. At 13 °C, SLA and leaf C:N were higher under interspecific and intraspecific interactions than without interaction, whereas at 23 °C, these traits increased under intraspecific interaction only. These effects only slightly improved the individual performance, suggesting that plant-plant interaction is an additional selective pressure on individuals in the context of climate warming. The decay rate of litter increased with the Leaf Carbon Content at 13 °C and 18 °C, but decreased at 23 °C. The highest decay rate was recorded at 18 °C. Besides, we observed evidence of positive feedback of the decay rate alone, and in interaction with the temperature, respectively on the leaf C:N and Leaf Dry Matter Content, suggesting that variations in ecological processes affect plant phenotypes. Our findings demonstrate that warming can directly and indirectly affect the evolutionary and ecological processes occurring in aquatic ecosystems through plants.

Global net biome CO2 exchange predicted comparably well using parameter-environment relationships and plant functional types

Accurate estimation and forecasts of net biome CO₂ exchange (NBE) are vital for understanding the role of terrestrial ecosystems in a changing climate. Prior efforts to improve NBE predictions have predominantly focused on increasing models' structural realism (and thus complexity), but parametric error and uncertainty are also key determinants of model skill. Here, we investigate how different parameterization assumptions propagate into NBE prediction errors across the globe, pitting the traditional plant functional type (PFT)-based approach against a novel top-down, machine learning-based "environmental filtering" (EF) approach. To do so, we simulate these contrasting methods for parameter assignment within a flexible model-data fusion framework of the terrestrial carbon cycle (CARDAMOM) at a global scale. In the PFT-based approach, model parameters from a small number of select locations are applied uniformly within regions sharing similar land cover characteristics. In the EF-based approach, a pixel's parameters are predicted based on underlying relationships with climate, soil, and canopy properties. To isolate the role of parametric from structural uncertainty in our analysis, we benchmark the resulting PFT-based and EF-based NBE predictions with estimates from CARDAMOM's Bayesian optimization approach (whereby "true" parameters consistent with a suite of data constraints are retrieved on a pixel-by-pixel basis). When considering the mean absolute error of NBE predictions across time, we find that the EF-based approach matches or outperforms the PFT-based approach at 55% of pixels-a narrow majority. However, NBE estimates from the EF-based approach are susceptible to compensation between errors in component flux predictions and predicted parameters can align poorly with the assumed "true" values. Overall, though, the EF-based approach is comparable to conventional approaches and merits further investigation to better understand and resolve these limitations. This work provides insight into the relationship between terrestrial biosphere model performance and parametric uncertainty, informing efforts to improve model parameterization via PFT-free and trait-based approaches.

Conceptual methodological framework for the resilience of biogeochemical services to heavy metals stress

The idea of linking stressors, services providing units (SPUs), and ecosystem services (ES) is ubiquitous in the literature, although is currently not applied in areas contaminated with heavy metals (HMs), This integrative literature review introduces the general form of a deterministic conceptual model of the cross-scale effect of HMs on biogeochemical services by SPUs with a feedback loop, a cross-scale heuristic concept of resilience, and develops a method for applying the conceptual model. The objectives are 1) to identify the clusters of existing research about HMs effects on ES, biodiversity, and resilience to HMs stress, 2) to map the scientific fields needed for the conceptual model's implementation, identify institutional constraints for inter-disciplinary cooperation, and propose solutions to surpass them, 3) to describe how the complexity of the cause-effect chain is reflected in the research hypotheses and objectives and extract methodological consequences, and 4) to describe how the conceptual model can be implemented. A nested analysis by CiteSpace of a set of 16,176 articles extracted from the Web of Science shows that at the highest level of data aggregation there is a clear separation between the topics of functional traits, stoichiometry, and regulating services from the typical issues of the literature about HMs, biodiversity, and ES. Most of the resilience to HMs stress agenda focuses on microbial communities. General topics such as the biodiversity-ecosystem function relationship in contaminated areas are no longer dominant in the current research, as well as large-scale problems like watershed management. The number of Web of Science domains that include the analyzed articles is large (26 up to 87 domains with at least ten articles, depending on the sub-set), but thirteen domains account for 70-80% of the literature. The complexity of approaches regarding the cause-effect chain, the stressors, the biological and ecological hierarchical level and the management objectives was characterized by a detailed analysis of 60 selected reviews and 121 primary articles. Most primary articles approach short causal chains, and the number of hypotheses or objectives by article tends to be low, pointing out the need for portfolios of complementary research projects in coherent inter-disciplinary programs and innovation ecosystems to couple the ES and resilience problems in areas contaminated with HMs. One provides triggers for developing innovation ecosystems, examples of complementary research hypotheses, and an example of technology transfer. Finally one proposes operationalizing the conceptual methodological model in contaminated socio-ecological systems by a calibration, a sensitivity analysis, and a validation phase.

The influence of biofertilizers on leaf economics spectrum traits in a herbaceous crop

Microbial inoculations or 'biofertilizers' represent novel contributions to sustainable agriculture. While belowground mechanisms surrounding how biofertilizers enhance crop production are well described, their role in aboveground trait expression remains less well explored. We quantified infraspecific variation in leaf economics spectrum (LES) traits in response to 10 biofertilizer treatments in basil (Ocimum basiclicum) cultivated under hydroponic conditions. Multiple physiological (i.e. maximum photosynthesis rates (A), dark respiration (R), and leaf-level light compensation points) and morphological (i.e. leaf mass per area (LMA) and leaf thickness) traits varied significantly across microbial treatments. Following treatments, basil plants differentiated from one another along an infraspecific LES, with certain plants expressing more resource-acquiring LES trait values (i.e. high A, R, leaf N, and low LMA), versus others that expressed the opposite suite of resource-conserving LES trait values. Infraspecific trait covariation largely matched LES patterns observed among plants globally. Bivariate and multivariate trait analyses further revealed that certain treatments-namely those including closely related Bacillus and Brevibacillus species strains-increased leaf resource capture traits such as A and leaf N. Biofertilizers influence plant performance through a role in moderating infraspecific leaf trait variation, thereby suggesting aboveground leaf traits may be used to diagnose optimal biofertilizer formulations in basil and other crops.

Citizen science plant observations encode global trait patterns

Global maps of plant functional traits are essential for studying the dynamics of the terrestrial biosphere, yet the spatial distribution of trait measurements remains sparse. With the increasing popularity of species identification apps, citizen scientists contribute to growing vegetation data collections. The question emerges whether such opportunistic citizen science data can help map plant functional traits globally. Here we show that we can map global trait patterns by complementing vascular plant observations from the global citizen science project iNaturalist with measurements from the plant trait database TRY. We evaluate these maps using sPlotOpen, a global collection of vegetation plot data. Our results show high correlations between the iNaturalist- and sPlotOpen-based maps of up to 0.69 (r) and higher correlations than to previously published trait maps. As citizen science data collections continue to grow, we can expect them to play a significant role in further improving maps of plant functional traits.

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.

Future tree survival in European forests depends on understorey tree diversity

Climate change heavily threatens forest ecosystems worldwide and there is urgent need to understand what controls tree survival and forests stability. There is evidence that biodiversity can enhance ecosystem stability (Loreau and de Mazancourt in Ecol Lett 16:106-115, 2013; McCann in Nature 405:228-233, 2000), however it remains largely unclear whether this also holds for climate change and what aspects of biodiversity might be most important. Here we apply machine learning to outputs of a flexible-trait Dynamic Global Vegetation Model to unravel the effects of enhanced functional tree trait diversity and its sub-components on climate-change resistance of temperate forests ( http://www.pik-potsdam.de/~billing/video/Forest_Resistance_LPJmLFIT.mp4 ). We find that functional tree trait diversity enhances forest resistance. We explain this with 1. stronger complementarity effects (~ 25% importance) especially improving the survival of trees in the understorey of up to + 16.8% (± 1.6%) and 2. environmental and competitive filtering of trees better adapted to future climate (40-87% importance). We conclude that forests containing functionally diverse trees better resist and adapt to future conditions. In this context, we especially highlight the role of functionally diverse understorey trees as they provide the fundament for better survival of young trees and filtering of resistant tree individuals in the future.

Post-disturbance reorganization of forest ecosystems in a changing world

Forest ecosystems are strongly impacted by continuing climate change and increasing disturbance activity, but how forest dynamics will respond remains highly uncertain. Here, we argue that a short time window after disturbance (i.e., a discrete event that disrupts prevailing ecosystem structure and composition and releases resources) is pivotal for future forest development. Trees that establish during this reorganization phase can shape forest structure and composition for centuries, providing operational early indications of forest change. While forest change has been fruitfully studied through a lens of resilience, profound ecological changes can be masked by a resilience versus regime shift dichotomy. We present a framework for characterizing the full spectrum of change after disturbance, analyzing forest reorganization along dimensions of forest structure (number, size, and spatial arrangement of trees) and composition (identity and diversity of tree species). We propose four major pathways through which forest cover can persist but reorganize following disturbance: resilience (no change in structure and composition), restructuring (structure changes but composition does not), reassembly (composition changes but structure does not), and replacement (structure and composition both change). Regime shifts occur when vegetation structure and composition are altered so profoundly that the emerging trajectory leads to nonforest. We identify fundamental processes underpinning forest reorganization which, if disrupted, deflect ecosystems away from resilience. To understand and predict forest reorganization, assessing these processes and the traits modulating them is crucial. A new wave of experiments, measurements, and models emphasizing the reorganization phase will further the capacity to anticipate future forest dynamics.

Soil phosphorus drives plant trait variations in a mature subtropical forest

Earth system models are implementing soil phosphorus dynamic and plant functional traits to predict functional changes in global forests. However, the linkage between soil phosphorus and plant traits lacks empirical evidence, especially in mature forests. Here, we examined the soil phosphorus constraint on plant functional traits in a mature subtropical forest based on observations of 9943 individuals from 90 species in a 5-ha forest dynamic plot and 405 individuals from 15 species in an adjacent 10-year nutrient-addition experiment. We first confirmed a pervasive phosphorus limitation on subtropical tree growth based on leaf N:P ratios. Then, we found that soil phosphorus dominated multidimensional trait variations in the 5-ha forest dynamic plot. Soil phosphorus content explained 44% and 53% of the variance in the traits defining the main functional space across species and communities, respectively. Lastly, we found much stronger phosphorus effects on most plant functional traits than nitrogen at both species and community levels in the 10-year nutrient-addition experiment. This study provides evidence for the consistent pattern of soil phosphorus constraint on plant trait variations between the species and community levels in a mature evergreen broadleaf forest in the East Asian monsoon region. These findings shed light on the predominant role of soil phosphorus on plant functional trait variations in mature subtropical forests, providing new insights for models to incorporate soil phosphorus constraint in predicting future vegetation dynamics.

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.

Soil water availability and branch age explain variability in xylem safety of European beech in Central Europe

Xylem embolism resistance has been identified as a key trait with a causal relation to drought-induced tree mortality, but not much is known about its intra-specific trait variability (ITV) in dependence on environmental variation. We measured xylem safety and efficiency in 300 European beech (Fagus sylvatica L.) trees across 30 sites in Central Europe, covering a precipitation reduction from 886 to 522 mm year⁻¹. A broad range of variables that might affect embolism resistance in mature trees, including climatic and soil water availability, competition, and branch age, were examined. The average P₅₀ value varied by up to 1 MPa between sites. Neither climatic aridity nor structural variables had a significant influence on P₅₀. However, P₅₀ was less negative for trees with a higher soil water storage capacity, and positively related to branch age, while specific conductivity (K_s) was not significantly associated with either of these variables. The greatest part of the ITV for xylem safety and efficiency was attributed to random variability within populations. We conclude that the influence of site water availability on P₅₀ and K_s is low in European beech, and that the high degree of within-population variability for P₅₀, partly due to variation in branch age, hampers the identification of a clear environmental signal.

Understanding mast seeding for conservation and land management

Masting, the intermittent and synchronous production of large seed crops, can have profound consequences for plant populations and the food webs that are built on their seeds. For centuries, people have recorded mast crops because of their importance in managing wildlife populations. In the past 30 years, we have begun to recognize the importance of masting in conserving and managing many other aspects of the environment: promoting the regeneration of forests following fire or other disturbance, conserving rare plants, conscientiously developing the use of edible seeds as non-timber forest products, coping with the consequences of extinctions on seed dispersal, reducing the impacts of plant invasions with biological control, suppressing zoonotic diseases and preventing depredation of endemic fauna. We summarize current instances and future possibilities of a broad set of applications of masting. By exploring in detail several case studies, we develop new perspectives on how solutions to pressing conservation and land management problems may benefit by better understanding the dynamics of seed production. A lesson common to these examples is that masting can be used to time management, and often, to do this effectively, we need models that explicitly forecast masting and the dynamics of seed-eating animals into the near-term future. This article is part of the theme issue 'The ecology and evolution of synchronized seed production in plants'.

Verification of our empirical understanding of the physiology and ecology of two contrasting plantation species using a trait database

The effects of climate change on forest ecosystems take on increasing importance more than ever. Information on plant traits is a powerful predictor of ecosystem dynamics and functioning. We reviewed the major ecological traits, such as foliar gas exchange and nutrients, xylem morphology and drought tolerance, of Cryptomeria japonica and Chamaecyparis obtusa, which are major timber species in East Asia, especially in Japan, by using a recently developed functional trait database for both species (SugiHinokiDB). Empirically, C. obtusa has been planted under drier conditions, whereas C. japonica, which grows faster but thought to be less drought tolerant, has been planted under wetter conditions. Our analysis generally support the empirical knowledge: The maximum photosynthetic rate, stomatal conductance, foliar nutrient content and soil-to-foliage hydraulic conductance were higher in C. japonica than in C. obtusa. In contrast, the foliar turgor loss point and xylem pressure corresponding to 50% conductivity, which indicate drought tolerance, were lower in C. obtusa and are consistent with the drier habitat of C. obtusa. Ontogenetic shifts were also observed; as the age and height of the trees increased, foliar nutrient concentrations, foliar minimum midday water potential and specific leaf area decreased in C. japonica, suggesting that nutrient and water limitation occurs with the growth. In C. obtusa, the ontogenetic shits of these foliar traits were less pronounced. Among the Cupressaceae worldwide, the drought tolerance of C. obtusa, as well as C. japonica, was not as high. This may be related to the fact that the Japanese archipelago has historically not been subjected to strong dryness. The maximum photosynthetic rate showed intermediate values within the family, indicating that C. japonica and C. obtusa exhibit relatively high growth rates in the Cupressaceae family, and this is thought to be the reason why they have been selected as economically suitable timber species in Japanese forestry. This study clearly demonstrated that the plant trait database provides us a promising opportunity to verify out empirical knowledge of plantation management and helps us to understand effect of climate change on plantation forests by using trait-based modelling.

Spectroscopy outperforms leaf trait relationships for predicting photosynthetic capacity across different forest types

Leaf trait relationships are widely used to predict ecosystem function in terrestrial biosphere models (TBMs), in which leaf maximum carboxylation capacity (V_c,max ), an important trait for modelling photosynthesis, can be inferred from other easier-to-measure traits. However, whether trait-V_c,max relationships are robust across different forest types remains unclear. Here we used measurements of leaf traits, including one morphological trait (leaf mass per area), three biochemical traits (leaf water content, area-based leaf nitrogen content, and leaf chlorophyll content), one physiological trait (V_c,max ), as well as leaf reflectance spectra, and explored their relationships within and across three contrasting forest types in China. We found weak and forest type-specific relationships between V_c,max and the four morphological and biochemical traits (R²  ≤ 0.15), indicated by significantly changing slopes and intercepts across forest types. By contrast, reflectance spectroscopy effectively collapsed the differences in the trait-V_c,max relationships across three forest biomes into a single robust model for V_c,max (R²  = 0.77), and also accurately estimated the four traits (R²  = 0.75-0.94). These findings challenge the traditional use of the empirical trait-V_c,max relationships in TBMs for estimating terrestrial plant photosynthesis, but also highlight spectroscopy as an efficient alternative for characterising V_c,max and multitrait variability, with critical insights into ecosystem modelling and functional trait ecology.

Intraspecific trait variation in plants: a renewed focus on its role in ecological processes

BACKGROUND

Investigating the causes and consequences of intraspecific trait variation (ITV) in plants is not novel, as it has long been recognized that such variation shapes biotic and abiotic interactions. While evolutionary and population biology have extensively investigated ITV, only in the last 10 years has interest in ITV surged within community and comparative ecology.

SCOPE

Despite this recent interest, still lacking are thorough descriptions of ITV's extent, the spatial and temporal structure of ITV, and stronger connections between ITV and community and ecosystem properties. Our primary aim in this review is to synthesize the recent literature and ask: (1) How extensive is intraspecific variation in traits across scales, and what underlying mechanisms drive this variation? (2) How does this variation impact higher-order ecological processes (e.g. population dynamics, community assembly, invasion, ecosystem productivity)? (3) What are the consequences of ignoring ITV and how can these be mitigated? and (4) What are the most pressing research questions, and how can current practices be modified to suit our research needs? Our secondary aim is to target diverse and underrepresented traits and plant organs, including anatomy, wood, roots, hydraulics, reproduction and secondary chemistry. In addressing these aims, we showcase papers from the Special Issue.

CONCLUSIONS

Plant ITV plays a key role in determining individual and population performance, species interactions, community structure and assembly, and ecosystem properties. Its extent varies widely across species, traits and environments, and it remains difficult to develop a predictive model for ITV that is broadly applicable. Systematically characterizing the sources (e.g. ontogeny, population differences) of ITV will be a vital step forward towards identifying generalities and the underlying mechanisms that shape ITV. While the use of species means to link traits to higher-order processes may be appropriate in many cases, such approaches can obscure potentially meaningful variation. We urge the reporting of individual replicates and population means in online data repositories, a greater consideration of the mechanisms that enhance and constrain ITV's extent, and studies that span sub-disciplines.

Is Variation in Conspecific Negative Density Dependence Driving Tree Diversity Patterns at Large Scales?

Half a century ago, Janzen and Connell hypothesized that the high tree species diversity in tropical forests is maintained by specialized natural enemies. Along with other mechanisms, these can cause conspecific negative density dependence (CNDD) and thus maintain species diversity. Numerous studies have measured proxies of CNDD worldwide, but doubt about its relative importance remains. We find ample evidence for CNDD in local populations, but methodological limitations make it difficult to assess if CNDD scales up to control community diversity and thereby local and global biodiversity patterns. A combination of more robust statistical methods, new study designs, and eco-evolutionary models are needed to provide a more definite evaluation of the importance of CNDD for geographic variation in plant species diversity.

Assessing the response of forest productivity to climate extremes in Switzerland using model-data fusion

The response of forest productivity to climate extremes strongly depends on ambient environmental and site conditions. To better understand these relationships at a regional scale, we used nearly 800 observation years from 271 permanent long-term forest monitoring plots across Switzerland, obtained between 1980 and 2017. We assimilated these data into the 3-PG forest ecosystem model using Bayesian inference, reducing the bias of model predictions from 14% to 5% for forest stem carbon stocks and from 45% to 9% for stem carbon stock changes. We then estimated the productivity of forests dominated by Picea abies and Fagus sylvatica for the period of 1960-2018, and tested for productivity shifts in response to climate along elevational gradient and in extreme years. Simulated net primary productivity (NPP) decreased with elevation (2.86 ± 0.006 Mg C ha-1  year-1  km-1 for P. abies and 0.93 ± 0.010 Mg C ha-1  year-1  km-1 for F. sylvatica). During warm-dry extremes, simulated NPP for both species increased at higher and decreased at lower elevations, with reductions in NPP of more than 25% for up to 21% of the potential species distribution range in Switzerland. Reduced plant water availability had a stronger effect on NPP than temperature during warm-dry extremes. Importantly, cold-dry extremes had negative impacts on regional forest NPP comparable to warm-dry extremes. Overall, our calibrated model suggests that the response of forest productivity to climate extremes is more complex than simple shift toward higher elevation. Such robust estimates of NPP are key for increasing our understanding of forests ecosystems carbon dynamics under climate extremes.

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.