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Abstract Evapotranspiration (ET) is co‐regulated by subsurface water availability, atmospheric demand for water, and radiation. Spatial differences in the limiting factors on ET that emerge along the soil‐plant‐atmosphere continuum result in distinct ecohydrological regimes with differing sensitivities to atmospheric and subsurface drivers. However, different components of the soil‐plant‐atmosphere continuum are not equally well understood. Deep subsurface water access is particularly difficult to measure and model, but can sustain ET under drought conditions when shallow soil moisture appears to be acutely limiting. Here, we exploited this principle to identify ecosystems that rely on deep subsurface water availability. We first used a plant hydraulic model to determine the expected ET behavior for plants with deep water access. We then examined 19 flux towers and found that responsiveness of ET to atmospheric conditions during dry periods was indicative of some ecosystems with deep water access. We used the divergent sensitivities of ET to vapor pressure deficit, radiation, and shallow soil moisture to identify distinct ecohydrological regimes in gridded data covering the continental U.S. We diagnosed deep water usage in ecosystems where ET remained sensitive to atmospheric conditions despite being insensitive to shallow soil moisture variability. Further, we found that drought stress, plant hydraulic traits, and ecosystem biophysical variables mediated the sensitivity of ET to aboveground and belowground conditions. 

ABSTRACT Nature‐based climate solutions in Earth's forests could strengthen the land carbon sink and contribute to climate mitigation, but must adequately account for climate risks to the durability of carbon storage. Forest carbon offset protocols use a “buffer pool” to insure against disturbance risks that may compromise durability. However, the extent to which current buffer pool tools and allocations align with current scientific data or models is not well understood. Here, we use a tropical forest stand biomass model and an extensive set of long‐term tropical forest plots to test whether current buffer pool contributions are adequate to insure against observed disturbance regimes. We find that forest age and disturbance regime both influence necessary buffer pool sizes. In the majority of disturbance scenarios in a major carbon registry buffer pool tool, current buffer pools are substantially smaller than required by carbon cycle science. Buffer pool tools and estimates urgently need to be updated to accurately assess disturbance regimes and climate change impact on disturbances based on rigorous, open scientific datasets for nature‐based climate solutions to succeed. 

ABSTRACT The rapid increase in the volume and variety of terrestrial biosphere observations (i.e., remote sensing data and in situ measurements) offers a unique opportunity to derive ecological insights, refine process‐based models, and improve forecasting for decision support. However, despite their potential, ecological observations have primarily been used to benchmark process‐based models, as many past and current models lack the capability to directly integrate observations and their associated uncertainties for parameterization. In contrast, data assimilation frameworks such as the CARbon DAta MOdel fraMework (CARDAMOM) and its suite of process‐based models, known as the Data Assimilation Linked Ecosystem Carbon Model (DALEC), are specifically designed for model‐data fusion. This review, motivated by a recent CARDAMOM community workshop, examines the development and applications of CARDAMOM, with an emphasis on its role in advancing ecosystem process understanding. CARDAMOM employs a Bayesian approach, using a Markov Chain Monte Carlo algorithm to enable data‐driven calibration of DALEC parameters and initial states (i.e., carbon pool sizes) through observation operators. CARDAMOM's unique ability to retrieve localized model process parameters from diverse datasets—ranging from in situ measurements to global satellite observations—makes it a highly flexible tool for analyzing spatially variable ecosystem responses to environmental change. However, assimilating these data also presents challenges, including data quality issues that propagate into model skill, as well as trade‐offs between model complexity, parameter equifinality, and predictive performance. We discuss potential solutions to these challenges, such as reducing parameter equifinality by incorporating new observations. This review also offers community recommendations for incorporating emerging datasets, integrating machine learning techniques, strengthening collaboration with remote sensing, field, and modeling communities, and expanding CARDAMOM's relevance for localized ecosystem monitoring and decision‐making. CARDAMOM enables a deep, mechanistic understanding of terrestrial ecosystem dynamics that cannot be achieved through empirical analyses of observational datasets or weakly constrained models alone. 

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

Summary Observational evidence indicates that tree leaf area may acclimate in response to changes in water availability to alleviate hydraulic stress. However, the underlying mechanisms driving leaf area changes and consequences of different leaf area allocation strategies remain unknown.Here, we use a trait‐based hydraulically enabled tree model with two endmember leaf area allocation strategies, aimed at either maximizing carbon gain or moderating hydraulic stress. We examined the impacts of these strategies on future plant stress and productivity.Allocating leaf area to maximize carbon gain increased productivity with high CO₂, but systematically increased hydraulic stress. Following an allocation strategy to avoid increased future hydraulic stress missed out on 26% of the potential future net primary productivity in some geographies. Both endmember leaf area allocation strategies resulted in leaf area decreases under future climate scenarios, contrary to Earth system model (ESM) predictions.Leaf area acclimation to avoid increased hydraulic stress (and potentially the risk of accelerated mortality) was possible, but led to reduced carbon gain. Accounting for plant hydraulic effects on canopy acclimation in ESMs could limit or reverse current projections of future increases in leaf area, with consequences for the carbon and water cycles, and surface energy budgets. 

Abstract Forests are a large carbon sink and could serve as natural climate solutions that help moderate future warming. Thus, establishing forest carbon baselines is essential for tracking climate‐mitigation targets. Western US forests are natural climate solution hotspots but are profoundly threatened by drought and altered disturbance regimes. How these factors shape spatial patterns of carbon storage and carbon change over time is poorly resolved. Here, we estimate live and dead forest carbon density in 19 forested western US ecoregions with national inventory data (2005–2019) to determine: (a) current carbon distributions, (b) underpinning drivers, and (c) recent trends. Potential drivers of current carbon included harvest, wildfire, insect and disease, topography, and climate. Using random forests, we evaluated driver importance and relationships with current live and dead carbon within ecoregions. We assessed trends using linear models. Pacific Northwest (PNW) and Southwest (SW) ecoregions were most and least carbon dense, respectively. Climate was an important carbon driver in the SW and Lower Rockies. Fire reduced live and increased dead carbon, and was most important in the Upper Rockies and California. No ecoregion was unaffected by fire. Harvest and private ownership reduced carbon, particularly in the PNW. Since 2005, live carbon declined across much of the western US, likely from drought and fire. Carbon has increased in PNW ecoregions, likely recovering from past harvest, but recent record fire years may alter trajectories. Our results provide insight into western US forest carbon function and future vulnerabilities, which is vital for effective climate change mitigation strategies. 

Synopsis Classic debates in community ecology focused on the complexities of considering an ecosystem as a super-organ or organism. New consideration of such perspectives could clarify mechanisms underlying the dynamics of forest carbon dioxide (CO2) uptake and water vapor loss, important for predicting and managing the future of Earth’s ecosystems and climate system. Here, we provide a rubric for considering ecosystem traits as aggregated, systemic, or emergent, i.e., representing the ecosystem as an aggregate of its individuals or as a metaphorical or literal super-organ or organism. We review recent approaches to scaling-up plant water relations (hydraulics) concepts developed for organs and organisms to enable and interpret measurements at ecosystem-level. We focus on three community-scale versions of water relations traits that have potential to provide mechanistic insight into climate change responses of forest CO2 and H2O gas exchange and productivity: leaf water potential (Ψcanopy), pressure volume curves (eco-PV), and hydraulic conductance (Keco). These analyses can reveal additional ecosystem-scale parameters analogous to those typically quantified for leaves or plants (e.g., wilting point and hydraulic vulnerability) that may act as thresholds in forest responses to drought, including growth cessation, mortality, and flammability. We unite these concepts in a novel framework to predict Ψcanopy and its approaching of critical thresholds during drought, using measurements of Keco and eco-PV curves. We thus delineate how the extension of water relations concepts from organ- and organism-scales can reveal the hydraulic constraints on the interaction of vegetation and climate and provide new mechanistic understanding and prediction of forest water use and productivity. 

Abstract Spatiotemporal patterns of plant water uptake, loss, and storage exert a first‐order control on photosynthesis and evapotranspiration. Many studies of plant responses to water stress have focused on differences between species because of their different stomatal closure, xylem conductance, and root traits. However, several other ecohydrological factors are also relevant, including soil hydraulics, topographically driven redistribution of water, plant adaptation to local climatic variations, and changes in vegetation density. Here, we seek to understand the relative importance of the dominant species for regional‐scale variations in woody plant responses to water stress. We map plant water sensitivity (PWS) based on the response of remotely sensed live fuel moisture content to variations in hydrometeorology using an auto‐regressive model. Live fuel moisture content dynamics are informative of PWS because they directly reflect vegetation water content and therefore patterns of plant water uptake and evapotranspiration. The PWS is studied using 21,455 wooded locations containing U.S. Forest Service Forest Inventory and Analysis plots across the western United States, where species cover is known and where a single species is locally dominant. Using a species‐specific mean PWS value explains 23% of observed PWS variability. By contrast, a random forest driven by mean vegetation density, mean climate, soil properties, and topographic descriptors explains 43% of observed PWS variability. Thus, the dominant species explains only 53% (23% compared to 43%) of explainable variations in PWS. Mean climate and mean NDVI also exert significant influence on PWS. Our results suggest that studies of differences between species should explicitly consider the environments (climate, soil, topography) in which observations for each species are made, and whether those environments are representative of the entire species range. 

Summary Predictive relationships between plant traits and environmental factors can be derived at global and regional scales, informing efforts to reorient ecological models around functional traits. However, in a changing climate, the environmental variables used as predictors in such relationships are far from stationary. This could yield errors in trait–environment model predictions if timescale is not accounted for.Here, the timescale dependence of trait–environment relationships is investigated by regressingin situtrait measurements of specific leaf area, leaf nitrogen content, and wood density on local climate characteristics summarized across several increasingly long timescales.We identify contrasting responses of leaf and wood traits to climate timescale. Leaf traits are best predicted by recent climate timescales, while wood density is a longer term memory trait. The use of sub‐optimal climate timescales reduces the accuracy of the resulting trait–environment relationships.This study concludes that plant traits respond to climate conditions on the timescale of tissue lifespans rather than long‐term climate normals, even at large spatial scales where multiple ecological and physiological mechanisms drive trait change. Thus, determining trait–environment relationships with temporally relevant climate variables may be critical for predicting trait change in a nonstationary climate system. 

Abstract 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.