Leaf trait coordination and variation of blue oak across topo-environmental scales

Trees are arguably the most diverse and complex macro-organisms on Earth. The equally diverse functions of trees directly impact fluxes of carbon, water and energy from the land surface. A number of recent studies have shed light on the substantial within-species variability across plant traits, including aspects of leaf morphology and plant allocation of photosynthates to leaf biomass. Yet, within-tree variability in leaf traits due to microclimatic variations, leaf hydraulic coordination across traits at different physiological scales and variations in leaf traits over a growing season remain poorly studied. This knowledge gap is stymieing the fundamental understanding of what drives trait variation and covariation from tissues to trees to landscapes. Here, we present an extensive dataset measuring within-tree heterogeneity in leaf traits in California's blue oak (Quercus douglasii) across an edaphic gradient and over the course of a growing season at an oak-grass savanna in Southern CA, USA. We found a high level of within-tree crown leaf area:sapwood area variation that was not attributable to sample height or aspect. We also found a higher level of trait integration at the tree level, rather than branch level, suggesting that trees optimize water use at the organismal level. Despite the large variance in traits within a tree crown and across trees, we did not find strong evidence for adaptive plasticity or acclimation in leaf morphological traits (e.g., changes to phenotype which increased fitness) across temporal and spatial water availability gradients. Collectively, our results highlight strong variation in drought-related physiology, but limited evidence for adaptive trait plasticity over shorter time scales.

Observed forest trait velocities have not kept pace with hydraulic stress from climate change

The extent to which future climate change will increase forest stress and the amount to which species and forest ecosystems can acclimate or adapt to increased stress is a major unknown. We used high-resolution maps of hydraulic traits representing the diversity in tree drought tolerance across the United States, a hydraulically enabled tree model, and forest inventory observations of demographic shifts to quantify the ability for within-species acclimation and between-species range shifts to mediate climate stress. We found that forests are likely to experience increases in both acute and chronic hydraulic stress with climate change. Based on current species distributions, regional hydraulic trait diversity was sufficient to buffer against increased stress in 88% of forested areas. However, observed trait velocities in 81% of forested areas are not keeping up with the rate required to ameliorate projected future stress without leaf area acclimation.

Linking remotely sensed ecosystem resilience with forest mortality across the continental United States

Episodes of forest mortality have been observed worldwide associated with climate change, impacting species composition and ecosystem services such as water resources and carbon sequestration. Yet our ability to predict forest mortality remains limited, especially across large scales. Time series of satellite imagery has been used to document ecosystem resilience globally, but it is not clear how well remotely sensed resilience can inform the prediction of forest mortality across continental, multi-biome scales. Here, we leverage forest inventories across the continental United States to systematically assess the potential of ecosystem resilience derived using different data sets and methods to predict forest mortality. We found high resilience was associated with low mortality in eastern forests but was associated with high mortality in western regions. The unexpected resilience-mortality relation in western United States may be due to several factors including plant trait acclimation, insect population dynamics, or resource competition. Overall, our results not only supported the opportunity to use remotely sensed ecosystem resilience to predict forest mortality but also highlighted that ecological factors may have crucial influences because they can reverse the sign of the resilience-mortality relationships.

Open science priorities for rigorous nature-based climate solutions

Nature-based climate solutions (NbCS) hold promise, but must be based on the best available science to be successful. We outline key ingredients of open data and science crucial for robust and scalable nature-based climate solutions efforts, as an urgent call to action for academic researchers, nongovernmental organizations, government agencies, and private companies.

Tallo: A global tree allometry and crown architecture database

Data capturing multiple axes of tree size and shape, such as a tree's stem diameter, height and crown size, underpin a wide range of ecological research-from developing and testing theory on forest structure and dynamics, to estimating forest carbon stocks and their uncertainties, and integrating remote sensing imagery into forest monitoring programmes. However, these data can be surprisingly hard to come by, particularly for certain regions of the world and for specific taxonomic groups, posing a real barrier to progress in these fields. To overcome this challenge, we developed the Tallo database, a collection of 498,838 georeferenced and taxonomically standardized records of individual trees for which stem diameter, height and/or crown radius have been measured. These data were collected at 61,856 globally distributed sites, spanning all major forested and non-forested biomes. The majority of trees in the database are identified to species (88%), and collectively Tallo includes data for 5163 species distributed across 1453 genera and 187 plant families. The database is publicly archived under a CC-BY 4.0 licence and can be access from: https://doi.org/10.5281/zenodo.6637599. To demonstrate its value, here we present three case studies that highlight how the Tallo database can be used to address a range of theoretical and applied questions in ecology-from testing the predictions of metabolic scaling theory, to exploring the limits of tree allometric plasticity along environmental gradients and modelling global variation in maximum attainable tree height. In doing so, we provide a key resource for field ecologists, remote sensing researchers and the modelling community working together to better understand the role that trees play in regulating the terrestrial carbon cycle.

Reimagine fire science for the anthropocene

Fire is an integral component of ecosystems globally and a tool that humans have harnessed for millennia. Altered fire regimes are a fundamental cause and consequence of global change, impacting people and the biophysical systems on which they depend. As part of the newly emerging Anthropocene, marked by human-caused climate change and radical changes to ecosystems, fire danger is increasing, and fires are having increasingly devastating impacts on human health, infrastructure, and ecosystem services. Increasing fire danger is a vexing problem that requires deep transdisciplinary, trans-sector, and inclusive partnerships to address. Here, we outline barriers and opportunities in the next generation of fire science and provide guidance for investment in future research. We synthesize insights needed to better address the long-standing challenges of innovation across disciplines to (i) promote coordinated research efforts; (ii) embrace different ways of knowing and knowledge generation; (iii) promote exploration of fundamental science; (iv) capitalize on the "firehose" of data for societal benefit; and (v) integrate human and natural systems into models across multiple scales. Fire science is thus at a critical transitional moment. We need to shift from observation and modeled representations of varying components of climate, people, vegetation, and fire to more integrative and predictive approaches that support pathways toward mitigating and adapting to our increasingly flammable world, including the utilization of fire for human safety and benefit. Only through overcoming institutional silos and accessing knowledge across diverse communities can we effectively undertake research that improves outcomes in our more fiery future.

Climate and hydraulic traits interact to set thresholds for liana viability

Lianas, or woody vines, and trees dominate the canopy of tropical forests and comprise the majority of tropical aboveground carbon storage. These growth forms respond differently to contemporary variation in climate and resource availability, but their responses to future climate change are poorly understood because there are very few predictive ecosystem models representing lianas. We compile a database of liana functional traits (846 species) and use it to parameterize a mechanistic model of liana-tree competition. The substantial difference between liana and tree hydraulic conductivity represents a critical source of inter-growth form variation. Here, we show that lianas are many times more sensitive to drying atmospheric conditions than trees as a result of this trait difference. Further, we use our competition model and projections of tropical hydroclimate based on Representative Concentration Pathway 4.5 to show that lianas are more susceptible to reaching a hydraulic threshold for viability by 2100.

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.

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.

Turgor-limited predictions of tree growth, height and metabolic scaling over tree lifespans

Increasing evidence suggests that tree growth is sink-limited by environmental and internal controls rather than by carbon availability. However, the mechanisms underlying sink-limitations are not fully understood and thus not represented in large-scale vegetation models. We develop a simple, analytically solved, mechanistic, turgor-driven growth model (TDGM) and a phloem transport model (PTM) to explore the mechanics of phloem transport and evaluate three hypotheses. First, phloem transport must be explicitly considered to accurately predict turgor distributions and thus growth. Second, turgor-limitations can explain growth-scaling with size (metabolic scaling). Third, turgor can explain realistic growth rates and increments. We show that mechanistic, sink-limited growth schemes based on plant turgor limitations are feasible for large-scale model implementations with minimal computational demands. Our PTM predicted nearly uniform sugar concentrations along the phloem transport path regardless of phloem conductance, stem water potential gradients and the strength of sink-demands contrary to our first hypothesis, suggesting that phloem transport is not limited generally by phloem transport capacity per se but rather by carbon demand for growth and respiration. These results enabled TDGM implementation without explicit coupling to the PTM, further simplifying computation. We test the TDGM by comparing predictions of whole-tree growth rate to well-established observations (site indices) and allometric theory. Our simple TDGM predicts realistic tree heights, growth rates and metabolic scaling over decadal to centurial timescales, suggesting that tree growth is generally sink and turgor limited. Like observed trees, our TDGM captures tree-size- and resource-based deviations from the classical ¾ power-law metabolic scaling for which turgor is responsible.

Systematic over-crediting in California's forest carbon offsets program

Carbon offsets are widely used by individuals, corporations, and governments to mitigate their greenhouse gas emissions on the assumption that offsets reflect equivalent climate benefits achieved elsewhere. These climate-equivalence claims depend on offsets providing real and additional climate benefits beyond what would have happened, counterfactually, without the offsets project. Here, we evaluate the design of California's prominent forest carbon offsets program and demonstrate that its climate-equivalence claims fall far short on the basis of directly observable evidence. By design, California's program awards large volumes of offset credits to forest projects with carbon stocks that exceed regional averages. This paradigm allows for adverse selection, which could occur if project developers preferentially select forests that are ecologically distinct from unrepresentative regional averages. By digitizing and analyzing comprehensive offset project records alongside detailed forest inventory data, we provide direct evidence that comparing projects against coarse regional carbon averages has led to systematic over-crediting of 30.0 million tCO₂ e (90% CI: 20.5-38.6 million tCO₂ e) or 29.4% of the credits we analyzed (90% CI: 20.1%-37.8%). These excess credits are worth an estimated $410 million (90% CI: $280-$528 million) at recent market prices. Rather than improve forest management to store additional carbon, California's forest offsets program creates incentives to generate offset credits that do not reflect real climate benefits.

Climate change impacts plant carbon balance, increasing mean future carbon use efficiency but decreasing total forest extent at dry range edges

Carbon use efficiency (CUE) represents how efficient a plant is at translating carbon gains through gross primary productivity (GPP) into net primary productivity (NPP) after respiratory costs (R_a ). CUE varies across space with climate and species composition, but how CUE will respond to climate change is largely unknown due to uncertainty in R_a at novel high temperatures. We use a plant physiological model validated against global CUE observations and LIDAR vegetation canopy height data and find that model-predicted decreases in CUE are diagnostic of transitions from forests to shrubland at dry range edges. Under future climate scenarios, we show mean growing season CUE increases in core forested areas, but forest extent decreases at dry range edges, with substantial uncertainty in absolute CUE due to uncertainty in R_a . Our results highlight that future forest resilience is nuanced and controlled by multiple competing mechanisms.

Testing the effects of species interactions and water limitation on tree seedling biomass allocation and physiology

Species interactions mediate tree responses to water limitation because competition and/or facilitation alter plant physiology and growth. However, because it is difficult to isolate the effects of plant-plant interactions and water limitation from other environmental factors, the mechanisms underlying tree physiology and growth in coexisting plants under drought are poorly understood. We investigated how species interactions and water limitation impact the physiology and growth of trembling aspen (Populus tremuloides), narrowleaf cottonwood (Populus angustifolia) and ponderosa pine (Pinus ponderosa) seedlings in a controlled environment growth chamber, using aspen as a focal species. Seedlings were grown in pots alone or with a con- or hetero-specific seedling, and were subjected to a water limitation treatment. Growth, water status and physiological traits were measured before, during and after the treatment. Under well-watered conditions, the presence of another seedling affected growth or biomass allocation in all species, but did not impact the physiological traits we measured. Under water limitation, the presence of a competing seedling had a marginal impact on seedling growth and physiological traits in all species. Throughout the study, the magnitude and direction of seedling responses were complex and often species-specific. Our study serves as an important step toward testing how species' interactions modify physiological responses and growth in well-watered and water-limited periods.

Coupled whole-tree optimality and xylem hydraulics explain dynamic biomass partitioning

Trees partition biomass in response to resource limitation and physiological activity. It is presumed that these strategies evolved to optimize some measure of fitness. If the optimization criterion can be specified, then allometry can be modeled from first principles without prescribed parameterization. We present the Tree Hydraulics and Optimal Resource Partitioning (THORP) model, which optimizes allometry by estimating allocation fractions to organs as proportional to their ratio of marginal gain to marginal cost, where gain is net canopy photosynthesis rate, and costs are senescence rates. Root total biomass and profile shape are predicted simultaneously by a unified optimization. Optimal partitioning is solved by a numerically efficient analytical solution. THORP's predictions agree with reported tree biomass partitioning in response to size, water limitations, elevated CO₂ and pruning. Roots were sensitive to soil moisture profiles and grew down to the groundwater table when present. Groundwater buffered against water stress regardless of meteorology, stabilizing allometry and root profiles as deep as c. 30 m. Much of plant allometry can be explained by hydraulic considerations. However, nutrient limitations cannot be fully ignored. Rooting mass and profiles were synchronized with hydrological conditions and groundwater even at considerable depths, illustrating that the below ground shapes whole-tree allometry.

Understanding and predicting forest mortality in the western United States using long-term forest inventory data and modeled hydraulic damage

Global warming is expected to exacerbate the duration and intensity of droughts in the western United States, which may lead to increased tree mortality. A prevailing proximal mechanism of drought-induced tree mortality is hydraulic damage, but predicting tree mortality from hydraulic theory and climate data still remains a major scientific challenge. We used forest inventory data and a plant hydraulic model (HM) to address three questions: can we capture regional patterns of drought-induced tree mortality with HM-predicted damage thresholds; do HM metrics improve predictions of mortality across broad spatial areas; and what are the dominant controls of forest mortality when considering stand characteristics, climate metrics, and simulated hydraulic stress? We found that the amount of variance explained by models predicting mortality was limited (R² median = 0.10, R² range: 0.00-0.52). HM outputs, including hydraulic damage and carbon assimilation diagnostics, moderately improve mortality prediction across the western US compared with models using stand and climate predictors alone. Among factors considered, metrics of stand density and tree size tended to be some of the most critical factors explaining mortality, probably highlighting the important roles of structural overshoot, stand development, and biotic agent host selection and outbreaks in mortality patterns.

Optimization theory explains nighttime stomatal responses

Nocturnal transpiration is widely observed across species and biomes, and may significantly impact global water, carbon, and energy budgets. However, it remains elusive why plants lose water at night and how to model it at large scales. We hypothesized that plants optimize nighttime leaf diffusive conductance (g_wn ) to balance potential daytime photosynthetic benefits and nocturnal transpiration benefits. We quantified nighttime benefits from respiratory reductions due to evaporative leaf cooling. We described nighttime costs in terms of a reduced carbon gain during the day because of water use at night. We measured nighttime stomatal responses and tested our model with water birch (Betula occidentalis) saplings grown in a glasshouse. The g_wn of water birch decreased with drier soil, higher atmospheric CO₂ , wetter air, lower leaf temperature, and lower leaf respiration rate. Our model predicted all these responses correctly, except for the response of g_wn to air humidity. Our results also suggested that the slow decrease in g_wn after sunset could be associated with decreasing leaf respiration. The optimality-based nocturnal transpiration model smoothly integrates with daytime stomatal optimization approaches, and thus has the potential to quantitatively predict nocturnal transpiration across space and time.

Integrating the evidence for a terrestrial carbon sink caused by increasing atmospheric CO2

Atmospheric carbon dioxide concentration ([CO₂ ]) is increasing, which increases leaf-scale photosynthesis and intrinsic water-use efficiency. These direct responses have the potential to increase plant growth, vegetation biomass, and soil organic matter; transferring carbon from the atmosphere into terrestrial ecosystems (a carbon sink). A substantial global terrestrial carbon sink would slow the rate of [CO₂ ] increase and thus climate change. However, ecosystem CO₂ responses are complex or confounded by concurrent changes in multiple agents of global change and evidence for a [CO₂ ]-driven terrestrial carbon sink can appear contradictory. Here we synthesize theory and broad, multidisciplinary evidence for the effects of increasing [CO₂ ] (iCO₂ ) on the global terrestrial carbon sink. Evidence suggests a substantial increase in global photosynthesis since pre-industrial times. Established theory, supported by experiments, indicates that iCO₂ is likely responsible for about half of the increase. Global carbon budgeting, atmospheric data, and forest inventories indicate a historical carbon sink, and these apparent iCO₂ responses are high in comparison to experiments and predictions from theory. Plant mortality and soil carbon iCO₂ responses are highly uncertain. In conclusion, a range of evidence supports a positive terrestrial carbon sink in response to iCO₂ , albeit with uncertain magnitude and strong suggestion of a role for additional agents of global change.

A theoretical and empirical assessment of stomatal optimization modeling

Optimal stomatal control models have shown great potential in predicting stomatal behavior and improving carbon cycle modeling. Basic stomatal optimality theory posits that stomatal regulation maximizes the carbon gain relative to a penalty of stomatal opening. All models take a similar approach to calculate instantaneous carbon gain from stomatal opening (the gain function). Where the models diverge is in how they calculate the corresponding penalty (the penalty function). In this review, we compare and evaluate 10 different optimization models in how they quantify the penalty and how well they predict stomatal responses to the environment. We evaluate models in two ways. First, we compare their penalty functions against seven criteria that ensure a unique and qualitatively realistic solution. Second, we quantitatively test model against multiple leaf gas-exchange datasets. The optimization models with better predictive skills have penalty functions that meet our seven criteria and use fitting parameters that are both few in number and physiology based. The most skilled models are those with a penalty function based on stress-induced hydraulic failure. We conclude by proposing a new model that has a hydraulics-based penalty function that meets all seven criteria and demonstrates a highly predictive skill against our test datasets.

Forecasting semi-arid biome shifts in the Anthropocene

Shrub encroachment, forest decline and wildfires have caused large-scale changes in semi-arid vegetation over the past 50 years. Climate is a primary determinant of plant growth in semi-arid ecosystems, yet it remains difficult to forecast large-scale vegetation shifts (i.e. biome shifts) in response to climate change. We highlight recent advances from four conceptual perspectives that are improving forecasts of semi-arid biome shifts. Moving from small to large scales, first, tree-level models that simulate the carbon costs of drought-induced plant hydraulic failure are improving predictions of delayed-mortality responses to drought. Second, tracer-informed water flow models are improving predictions of species coexistence as a function of climate. Third, new applications of ecohydrological models are beginning to simulate small-scale water movement processes at large scales. Fourth, remotely-sensed measurements of plant traits such as relative canopy moisture are providing early-warning signals that predict forest mortality more than a year in advance. We suggest that a community of researchers using modeling approaches (e.g. machine learning) that can integrate these perspectives will rapidly improve forecasts of semi-arid biome shifts. Better forecasts can be expected to help prevent catastrophic changes in vegetation states by identifying improved monitoring approaches and by prioritizing high-risk areas for management.

Competition and Drought Alter Optimal Stomatal Strategy in Tree Seedlings

A better understanding of plant stomatal strategies holds strong promise for improving predictions of vegetation responses to drought because stomata are the primary mechanism through which plants mitigate water stress. It has been assumed that plants regulate stomata to maintain a constant marginal water use efficiency and forego carbon gain when water is scarce. However, recent hypotheses pose that plants maximize carbon assimilation while also accounting for the risk of hydraulic damage via cavitation and hydraulic failure. This "gain-risk" framework incorporates competition in stomatal regulation because it takes into account that neighboring plants can "steal" unused water. This study utilizes stomatal models representing both the water use efficiency and carbon-maximization frameworks, and empirical data from three species in a potted growth chamber experiment, to investigate the effects of drought and competition on seedling stomatal strategy. We found that drought and competition responses in the empirical data were best explained by the carbon-maximization hypothesis and that both drought and competition affected stomatal strategy. Interestingly, stomatal responses differed substantially by species, with seedlings employing a riskier strategy when planted with a high water use competitor, and seedlings employing a more conservative strategy when planted with a low water use competitor. Lower water users in general had less stomatal sensitivity to decreasing Ψ _L compared to moderate to high water users. Repeated water stress also resulted in legacy effects on plant stomatal behavior, increasing stomatal sensitivity (i.e., conservative behavior) even when the seedling was returned to well-watered conditions. These results indicate that stomatal strategies are dynamic and change with climate and competition stressors. Therefore, incorporating mechanisms that allow for stomatal behavioral changes in response to water limitation may be an important step to improving carbon cycle projections in coupled climate-Earth system models.

Pervasive decreases in living vegetation carbon turnover time across forest climate zones

Forests play a major role in the global carbon cycle. Previous studies on the capacity of forests to sequester atmospheric CO2 have mostly focused on carbon uptake, but the roles of carbon turnover time and its spatiotemporal changes remain poorly understood. Here, we used long-term inventory data (1955 to 2018) from 695 mature forest plots to quantify temporal trends in living vegetation carbon turnover time across tropical, temperate, and cold climate zones, and compared plot data to 8 Earth system models (ESMs). Long-term plots consistently showed decreases in living vegetation carbon turnover time, likely driven by increased tree mortality across all major climate zones. Changes in living vegetation carbon turnover time were negatively correlated with CO2 enrichment in both forest plot data and ESM simulations. However, plot-based correlations between living vegetation carbon turnover time and climate drivers such as precipitation and temperature diverged from those of ESM simulations. Our analyses suggest that forest carbon sinks are likely to be constrained by a decrease in living vegetation carbon turnover time, and accurate projections of forest carbon sink dynamics will require an improved representation of tree mortality processes and their sensitivity to climate in ESMs.

Leveraging plant hydraulics to yield predictive and dynamic plant leaf allocation in vegetation models with climate change

Plant functional traits provide a link in process-based vegetation models between plant-level physiology and ecosystem-level responses. Recent advances in physiological understanding and computational efficiency have allowed for the incorporation of plant hydraulic processes in large-scale vegetation models. However, a more mechanistic representation of water limitation that determines ecosystem responses to plant water stress necessitates a re-evaluation of trait-based constraints for plant carbon allocation, particularly allocation to leaf area. In this review, we examine model representations of plant allocation to leaves, which is often empirically set by plant functional type-specific allometric relationships. We analyze the evolution of the representation of leaf allocation in models of different scales and complexities. We show the impacts of leaf allocation strategy on plant carbon uptake in the context of recent advancements in modeling hydraulic processes. Finally, we posit that deriving allometry from first principles using mechanistic hydraulic processes is possible and should become standard practice, rather than using prescribed allometries. The representation of allocation as an emergent property of scarce resource constraints is likely to be critical to representing how global change processes impact future ecosystem dynamics and carbon fluxes and may reduce the number of poorly constrained parameters in vegetation models.

Widespread drought-induced tree mortality at dry range edges indicates that climate stress exceeds species' compensating mechanisms

Drought-induced tree mortality is projected to increase due to climate change, which will have manifold ecological and societal impacts including the potential to weaken or reverse the terrestrial carbon sink. Predictions of tree mortality remain limited, in large part because within-species variations in ecophysiology due to plasticity or adaptation and ecosystem adjustments could buffer mortality in dry locations. Here, we conduct a meta-analysis of 50 studies spanning >100 woody plant species globally to quantify how populations within species vary in vulnerability to drought mortality and whether functional traits or climate mediate mortality patterns. We find that mortality predominantly occurs in drier populations and this pattern is more pronounced in species with xylem that can tolerate highly negative water potentials, typically considered to be an adaptive trait for dry regions, and species that experience higher variability in water stress. Our results indicate that climate stress has exceeded physiological and ecosystem-level tolerance or compensating mechanisms by triggering extensive mortality at dry range edges and provides a foundation for future mortality projections in empirical distribution and mechanistic vegetation models.

Climate and plant trait strategies determine tree carbon allocation to leaves and mediate future forest productivity

Forest leaf area has enormous leverage on the carbon cycle because it mediates both forest productivity and resilience to climate extremes. Despite widespread evidence that trees are capable of adjusting to changes in environment across both space and time through modifying carbon allocation to leaves, many vegetation models use fixed carbon allocation schemes independent of environment, which introduces large uncertainties into predictions of future forest responses to atmospheric CO₂ fertilization and anthropogenic climate change. Here, we develop an optimization-based model, whereby tree carbon allocation to leaves is an emergent property of environment and plant hydraulic traits. Using a combination of meta-analysis, observational datasets, and model predictions, we find strong evidence that optimal hydraulic-carbon coupling explains observed patterns in leaf allocation across large environmental and CO₂ concentration gradients. Furthermore, testing the sensitivity of leaf allocation strategy to a diversity in hydraulic and economic spectrum physiological traits, we show that plant hydraulic traits in particular have an enormous impact on the global change response of forest leaf area. Our results provide a rigorous theoretical underpinning for improving carbon cycle predictions through advancing model predictions of leaf area, and underscore that tree-level carbon allocation to leaves should be derived from first principles using mechanistic plant hydraulic processes in the next generation of vegetation models.

The stomatal response to rising CO2 concentration and drought is predicted by a hydraulic trait-based optimization model

Modeling stomatal control is critical for predicting forest responses to the changing environment and hence the global water and carbon cycles. A trait-based stomatal control model that optimizes carbon gain while avoiding hydraulic risk has been shown to perform well in response to drought. However, the model's performance against changes in atmospheric CO2, which is rising rapidly due to human emissions, has yet to be evaluated. The present study tested the gain-risk model's ability to predict the stomatal response to CO2 concentration with potted water birch (Betula occidentalis Hook.) saplings in a growth chamber. The model's performance in predicting stomatal response to changes in atmospheric relative humidity and soil moisture was also assessed. The gain-risk model predicted the photosynthetic assimilation, transpiration rate and leaf xylem pressure under different CO2 concentrations, having a mean absolute percentage error (MAPE) of 25%. The model also predicted the responses to relative humidity and soil drought with a MAPE of 21.9% and 41.9%, respectively. Overall, the gain-risk model had an MAPE of 26.8% compared with the 37.5% MAPE obtained by a standard empirical model of stomatal conductance. Importantly, unlike empirical models, the optimization model relies on measurable physiological traits as inputs and performs well in predicting responses to novel environmental conditions without empirical corrections. Incorporating the optimization model in larger scale models has the potential for improving the simulation of water and carbon cycles.

Plant functional traits and climate influence drought intensification and land-atmosphere feedbacks

The fluxes of energy, water, and carbon from terrestrial ecosystems influence the atmosphere. Land-atmosphere feedbacks can intensify extreme climate events like severe droughts and heatwaves because low soil moisture decreases both evaporation and plant transpiration and increases local temperature. Here, we combine data from a network of temperate and boreal eddy covariance towers, satellite data, plant trait datasets, and a mechanistic vegetation model to diagnose the controls of soil moisture feedbacks to drought. We find that climate and plant functional traits, particularly those related to maximum leaf gas exchange rate and water transport through the plant hydraulic continuum, jointly affect drought intensification. Our results reveal that plant physiological traits directly affect drought intensification and indicate that inclusion of plant hydraulic transport mechanisms in models may be critical for accurately simulating land-atmosphere feedbacks and climate extremes under climate change.

Hydraulic diversity of forests regulates ecosystem resilience during drought

Plants influence the atmosphere through fluxes of carbon, water and energy¹, and can intensify drought through land-atmosphere feedback effects^2-4. The diversity of plant functional traits in forests, especially physiological traits related to water (hydraulic) transport, may have a critical role in land-atmosphere feedback, particularly during drought. Here we combine 352 site-years of eddy covariance measurements from 40 forest sites, remote-sensing observations of plant water content and plant functional-trait data to test whether the diversity in plant traits affects the response of the ecosystem to drought. We find evidence that higher hydraulic diversity buffers variation in ecosystem flux during dry periods across temperate and boreal forests. Hydraulic traits were the predominant significant predictors of cross-site patterns in drought response. By contrast, standard leaf and wood traits, such as specific leaf area and wood density, had little explanatory power. Our results demonstrate that diversity in the hydraulic traits of trees mediates ecosystem resilience to drought and is likely to have an important role in future ecosystem-atmosphere feedback effects in a changing climate.

Differential declines in Alaskan boreal forest vitality related to climate and competition

Rapid warming and changes in water availability at high latitudes alter resource abundance, tree competition, and disturbance regimes. While these changes are expected to disrupt the functioning of boreal forests, their ultimate implications for forest composition are uncertain. In particular, recent site-level studies of the Alaskan boreal forest have reported both increases and decreases in productivity over the past few decades. Here, we test the idea that variations in Alaskan forest growth and mortality rates are contingent on species composition. Using forest inventory measurements and climate data from plots located throughout interior and south-central Alaska, we show significant growth and mortality responses associated with competition, midsummer vapor pressure deficit, and increased growing season length. The governing climate and competition processes differed substantially across species. Surprisingly, the most dramatic climate response occurred in the drought tolerant angiosperm species, trembling aspen, and linked high midsummer vapor pressure deficits to decreased growth and increased insect-related mortality. Given that species composition in the Alaskan and western Canadian boreal forests is projected to shift toward early-successional angiosperm species due to fire regime, these results underscore the potential for a reduction in boreal productivity stemming from increases in midsummer evaporative demand.

Vegetation demographics in Earth System Models: A review of progress and priorities

Numerous current efforts seek to improve the representation of ecosystem ecology and vegetation demographic processes within Earth System Models (ESMs). These developments are widely viewed as an important step in developing greater realism in predictions of future ecosystem states and fluxes. Increased realism, however, leads to increased model complexity, with new features raising a suite of ecological questions that require empirical constraints. Here, we review the developments that permit the representation of plant demographics in ESMs, and identify issues raised by these developments that highlight important gaps in ecological understanding. These issues inevitably translate into uncertainty in model projections but also allow models to be applied to new processes and questions concerning the dynamics of real-world ecosystems. We argue that stronger and more innovative connections to data, across the range of scales considered, are required to address these gaps in understanding. The development of first-generation land surface models as a unifying framework for ecophysiological understanding stimulated much research into plant physiological traits and gas exchange. Constraining predictions at ecologically relevant spatial and temporal scales will require a similar investment of effort and intensified inter-disciplinary communication.

Allometric equations for integrating remote sensing imagery into forest monitoring programmes

Remote sensing is revolutionizing the way we study forests, and recent technological advances mean we are now able - for the first time - to identify and measure the crown dimensions of individual trees from airborne imagery. Yet to make full use of these data for quantifying forest carbon stocks and dynamics, a new generation of allometric tools which have tree height and crown size at their centre are needed. Here, we compile a global database of 108753 trees for which stem diameter, height and crown diameter have all been measured, including 2395 trees harvested to measure aboveground biomass. Using this database, we develop general allometric models for estimating both the diameter and aboveground biomass of trees from attributes which can be remotely sensed - specifically height and crown diameter. We show that tree height and crown diameter jointly quantify the aboveground biomass of individual trees and find that a single equation predicts stem diameter from these two variables across the world's forests. These new allometric models provide an intuitive way of integrating remote sensing imagery into large-scale forest monitoring programmes and will be of key importance for parameterizing the next generation of dynamic vegetation models.