The impacts of rising vapour pressure deficit in natural and managed ecosystems.

An exponential rise in the atmospheric vapour pressure deficit (VPD) is among the most consequential impacts of climate change in terrestrial ecosystems. Rising VPD has negative and cascading effects on nearly all aspects of plant function including photosynthesis, water status, growth and survival. These responses are exacerbated by land-atmosphere interactions that couple VPD to soil water and govern the evolution of drought, affecting a range of ecosystem services including carbon uptake, biodiversity, the provisioning of water resources and crop yields. However, despite the global nature of this phenomenon, research on how to incorporate these impacts into resilient management regimes is largely in its infancy, due in part to the entanglement of VPD trends with those of other co-evolving climate drivers. Here, we review the mechanistic bases of VPD impacts at a range of spatial scales, paying particular attention to the independent and interactive influence of VPD in the context of other environmental changes. We then evaluate the consequences of these impacts within key management contexts, including water resources, croplands, wildfire risk mitigation and management of natural grasslands and forests. We conclude with recommendations describing how management regimes could be altered to mitigate the otherwise highly deleterious consequences of rising VPD.

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.

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.

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.

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 (Ra ). CUE varies across space with climate and species composition, but how CUE will respond to climate change is largely unknown due to uncertainty in Ra 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 Ra . Our results highlight that future forest resilience is nuanced and controlled by multiple competing mechanisms.

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.

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.

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.

Why is Tree Drought Mortality so Hard to Predict?

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

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.

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.

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 (gwn ) 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 gwn 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 gwn to air humidity. Our results also suggested that the slow decrease in gwn 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.

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.

Climate-driven risks to the climate mitigation potential of forests.

Forests have considerable potential to help mitigate human-caused climate change and provide society with many cobenefits. However, climate-driven risks may fundamentally compromise forest carbon sinks in the 21st century. Here, we synthesize the current understanding of climate-driven risks to forest stability from fire, drought, biotic agents, and other disturbances. We review how efforts to use forests as natural climate solutions presently consider and could more fully embrace current scientific knowledge to account for these climate-driven risks. Recent advances in vegetation physiology, disturbance ecology, mechanistic vegetation modeling, large-scale ecological observation networks, and remote sensing are improving current estimates and forecasts of the risks to forest stability. A more holistic understanding and quantification of such risks will help policy-makers and other stakeholders effectively use forests as natural climate solutions.

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.

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.

Trait velocities reveal that mortality has driven widespread coordinated shifts in forest hydraulic trait composition.

Understanding the driving mechanisms behind existing patterns of vegetation hydraulic traits and community trait diversity is critical for advancing predictions of the terrestrial carbon cycle because hydraulic traits affect both ecosystem and Earth system responses to changing water availability. Here, we leverage an extensive trait database and a long-term continental forest plot network to map changes in community trait distributions and quantify "trait velocities" (the rate of change in community-weighted traits) for different regions and different forest types across the United States from 2000 to the present. We show that diversity in hydraulic traits and photosynthetic characteristics is more related to local water availability than overall species diversity. Finally, we find evidence for coordinated shifts toward communities with more drought-tolerant traits driven by tree mortality, but the magnitude of responses differs depending on forest type. The hydraulic trait distribution maps provide a publicly available platform to fundamentally advance understanding of community trait change in response to climate change and predictive abilities of mechanistic vegetation models.