Assessing uncertainties in a second-generation dynamic vegetation model caused by ecological scale limitations

Processes and mechanisms of coastal woody-plant mortality

Observations of woody plant mortality in coastal ecosystems are globally widespread, but the overarching processes and underlying mechanisms are poorly understood. This knowledge deficiency, combined with rapidly changing water levels, storm surges, atmospheric CO₂ , and vapor pressure deficit, creates large predictive uncertainty regarding how coastal ecosystems will respond to global change. Here, we synthesize the literature on the mechanisms that underlie coastal woody-plant mortality, with the goal of producing a testable hypothesis framework. The key emergent mechanisms underlying mortality include hypoxic, osmotic, and ionic-driven reductions in whole-plant hydraulic conductance and photosynthesis that ultimately drive the coupled processes of hydraulic failure and carbon starvation. The relative importance of these processes in driving mortality, their order of progression, and their degree of coupling depends on the characteristics of the anomalous water exposure, on topographic effects, and on taxa-specific variation in traits and trait acclimation. Greater inundation exposure could accelerate mortality globally; however, the interaction of changing inundation exposure with elevated CO₂ , drought, and rising vapor pressure deficit could influence mortality likelihood. Models of coastal forests that incorporate the frequency and duration of inundation, the role of climatic drivers, and the processes of hydraulic failure and carbon starvation can yield improved estimates of inundation-induced woody-plant mortality.

Tree crown damage and its effects on forest carbon cycling in a tropical forest

Crown damage can account for over 23% of canopy biomass turnover in tropical forests and is a strong predictor of tree mortality; yet, it is not typically represented in vegetation models. We incorporate crown damage into the Functionally Assembled Terrestrial Ecosystem Simulator (FATES), to evaluate how lags between damage and tree recovery or death alter demographic rates and patterns of carbon turnover. We represent crown damage as a reduction in a tree's crown area and leaf and branch biomass, and allow associated variation in the ratio of aboveground to belowground plant tissue. We compare simulations with crown damage to simulations with equivalent instant increases in mortality and benchmark results against data from Barro Colorado Island (BCI), Panama. In FATES, crown damage causes decreases in growth rates that match observations from BCI. Crown damage leads to increases in carbon starvation mortality in FATES, but only in configurations with high root respiration and decreases in carbon storage following damage. Crown damage also alters competitive dynamics, as plant functional types that can recover from crown damage outcompete those that cannot. This is a first exploration of the trade-off between the additional complexity of the novel crown damage module and improved predictive capabilities. At BCI, a tropical forest that does not experience high levels of disturbance, both the crown damage simulations and simulations with equivalent increases in mortality does a reasonable job of capturing observations. The crown damage module provides functionality for exploring dynamics in forests with more extreme disturbances such as cyclones and for capturing the synergistic effects of disturbances that overlap in space and time.

Forest regeneration within Earth system models: current process representations and ways forward

Earth system models must predict forest responses to global change in order to simulate future global climate, hydrology, and ecosystem dynamics. These models are increasingly adopting vegetation demographic approaches that explicitly represent tree growth, mortality, and recruitment, enabling advances in the projection of forest vulnerability and resilience, as well as evaluation with field data. To date, simulation of regeneration processes has received far less attention than simulation of processes that affect growth and mortality, in spite of their critical role maintaining forest structure, facilitating turnover in forest composition over space and time, enabling recovery from disturbance, and regulating climate-driven range shifts. Our critical review of regeneration process representations within current Earth system vegetation demographic models reveals the need to improve parameter values and algorithms for reproductive allocation, dispersal, seed survival and germination, environmental filtering in the seedling layer, and tree regeneration strategies adapted to wind, fire, and anthropogenic disturbance regimes. These improvements require synthesis of existing data, specific field data-collection protocols, and novel model algorithms compatible with global-scale simulations. Vegetation demographic models offer the opportunity to more fully integrate ecological understanding into Earth system prediction; regeneration processes need to be a critical part of the effort.

JA, Sanchez‐Azofeifa GA, De Deurwaerder HPT, Krishna Moorthy SM, Schnitzer SA, Marvin DC, Longo M, Liu C, Broadbent EN, Almeyda Zambrano AM, Muller‐Landau HC, Detto M, Verbeeck H. Liana optical traits increase tropical forest albedo and reduce ecosystem productivity

Lianas are a key growth form in tropical forests. Their lack of self-supporting tissues and their vertical position on top of the canopy make them strong competitors of resources. A few pioneer studies have shown that liana optical traits differ on average from those of colocated trees. Those trait discrepancies were hypothesized to be responsible for the competitive advantage of lianas over trees. Yet, in the absence of reliable modelling tools, it is impossible to unravel their impact on the forest energy balance, light competition, and on the liana success in Neotropical forests. To bridge this gap, we performed a meta-analysis of the literature to gather all published liana leaf optical spectra, as well as all canopy spectra measured over different levels of liana infestation. We then used a Bayesian data assimilation framework applied to two radiative transfer models (RTMs) covering the leaf and canopy scales to derive tropical tree and liana trait distributions, which finally informed a full dynamic vegetation model. According to the RTMs inversion, lianas grew thinner, more horizontal leaves with lower pigment concentrations. Those traits made the lianas very efficient at light interception and significantly modified the forest energy balance and its carbon cycle. While forest albedo increased by 14% in the shortwave, light availability was reduced in the understorey (-30% of the PAR radiation) and soil temperature decreased by 0.5°C. Those liana-specific traits were also responsible for a significant reduction of tree (-19%) and ecosystem (-7%) gross primary productivity (GPP) while lianas benefited from them (their GPP increased by +27%). This study provides a novel mechanistic explanation to the increase in liana abundance, new evidence of the impact of lianas on forest functioning, and paves the way for the evaluation of the large-scale impacts of lianas on forest biogeochemical cycles.

A ddRADseq Survey of the Genetic Diversity of Rye (Secale cereale L.) Landraces from the Western Alps Reveals the Progressive Reduction of the Local Gene Pool

Rye (Secale cereale L.) has been at the basis of agriculture for centuries in most mountainous and northern areas of Eurasia, because it is more resistant than other cereals to low temperatures and poor soils. Rye deserves to be re-evaluated as a source of “environmentally resilient” genes in the future as well, and particularly in a perspective to grow cereals able to withstand global warming. According to recent studies, modern rye varieties have a relatively narrow genetic pool, a condition that is worsening in the most recent breeding processes. The preservation of local landraces as unique sources of genetic diversity has therefore become important, in order to preserve the genetic heritage of rye. In this study, genetic diversity of rye landraces collected in a sector of the Italian Alps particularly suited to traditional agriculture was investigated using the ddRADseq technique. A few landraces still managed with family farming turned out to be genetically distant from the commercial varieties currently in use, highlighting that the phenomenon of homogenization of the local genetic pool can be still circumvented. Ex situ conservation of genetically divergent landraces is a valid tool to avoid the dissipation of an as yet unexplored genetic potential.

Integrating terrestrial laser scanning with functional-structural plant models to investigate ecological and evolutionary processes of forest communities

BACKGROUND

Woody plants (trees and shrubs) play an important role in terrestrial ecosystems, but their size and longevity make them difficult subjects for traditional experiments. In the last 20 years functional-structural plant models (FSPMs) have evolved: they consider the interplay between plant modular structure, the immediate environment and internal functioning. However, computational constraints and data deficiency have long been limiting factors in a broader application of FSPMs, particularly at the scale of forest communities. Recently, terrestrial laser scanning (TLS), has emerged as an invaluable tool for capturing the 3-D structure of forest communities, thus opening up exciting opportunities to explore and predict forest dynamics with FSPMs.

SCOPE

The potential synergies between TLS-derived data and FSPMs have yet to be fully explored. Here, we summarize recent developments in FSPM and TLS research, with a specific focus on woody plants. We then evaluate the emerging opportunities for applying FSPMs in an ecological and evolutionary context, in light of TLS-derived data, with particular consideration of the challenges posed by scaling up from individual trees to whole forests. Finally, we propose guidelines for incorporating TLS data into the FSPM workflow to encourage overlap of practice amongst researchers.

CONCLUSIONS

We conclude that TLS is a feasible tool to help shift FSPMs from an individual-level modelling technique to a community-level one. The ability to scan multiple trees, of multiple species, in a short amount of time, is paramount to gathering the detailed structural information required for parameterizing FSPMs for forest communities. Conventional techniques, such as repeated manual forest surveys, have their limitations in explaining the driving mechanisms behind observed patterns in 3-D forest structure and dynamics. Therefore, other techniques are valuable to explore how forests might respond to environmental change. A robust synthesis between TLS and FSPMs provides the opportunity to virtually explore the spatial and temporal dynamics of forest communities.

Hydraulically-vulnerable trees survive on deep-water access during droughts in a tropical forest

Deep-water access is arguably the most effective, but under-studied, mechanism that plants employ to survive during drought. Vulnerability to embolism and hydraulic safety margins can predict mortality risk at given levels of dehydration, but deep-water access may delay plant dehydration. Here, we tested the role of deep-water access in enabling survival within a diverse tropical forest community in Panama using a novel data-model approach. We inversely estimated the effective rooting depth (ERD, as the average depth of water extraction), for 29 canopy species by linking diameter growth dynamics (1990-2015) to vapor pressure deficit, water potentials in the whole-soil column, and leaf hydraulic vulnerability curves. We validated ERD estimates against existing isotopic data of potential water-access depths. Across species, deeper ERD was associated with higher maximum stem hydraulic conductivity, greater vulnerability to xylem embolism, narrower safety margins, and lower mortality rates during extreme droughts over 35 years (1981-2015) among evergreen species. Species exposure to water stress declined with deeper ERD indicating that trees compensate for water stress-related mortality risk through deep-water access. The role of deep-water access in mitigating mortality of hydraulically-vulnerable trees has important implications for our predictive understanding of forest dynamics under current and future climates.

Inferring Grassland Drought Stress with Unsupervised Learning from Airborne Hyperspectral VNIR Imagery

The 2018–2019 Central European drought had a grave impact on natural and managed ecosystems, affecting their health and productivity. We examined patterns in hyperspectral VNIR imagery using an unsupervised learning approach to improve ecosystem monitoring and the understanding of grassland drought responses. The main objectives of this study were (1) to evaluate the application of simplex volume maximisation (SiVM), an unsupervised learning method, for the detection of grassland drought stress in high-dimensional remote sensing data at the ecosystem scale and (2) to analyse the contributions of different spectral plant and soil traits to the computed stress signal. The drought status of the research site was assessed with a non-parametric standardised precipitation–evapotranspiration index (SPEI) and soil moisture measurements. We used airborne HySpex VNIR-1800 data from spring 2018 and 2019 to compare vegetation condition at the onset of the drought with the state after one year. SiVM, an interpretable matrix factorisation technique, was used to derive typical extreme spectra (archetypes) from the hyperspectral data. The classification of archetypes allowed for the inference of qualitative drought stress levels. The results were evaluated using a set of geophysical measurements and vegetation indices as proxy variables for drought-inhibited vegetation growth. The successful application of SiVM for grassland stress detection at the ecosystem canopy scale was verified in a correlation analysis. The predictor importance was assessed with boosted beta regression. In the resulting interannual stress model, carotenoid-related variables had among the highest coefficient values. The significance of the photochemical reflectance index that uses 512 nm as reference wavelength (PRI512) demonstrates the value of combining imaging spectrometry and unsupervised learning for the monitoring of vegetation stress. It also shows the potential of archetypical reflectance spectra to be used for the remote estimation of photosynthetic efficiency. More conclusive results could be achieved by using vegetation measurements instead of proxy variables for evaluation. It must also be investigated how the method can be generalised across ecosystems.

Unraveling the relative role of light and water competition between lianas and trees in tropical forests: A vegetation model analysis

Despite their low contribution to forest carbon stocks, lianas (woody vines) play an important role in the carbon dynamics of tropical forests. As structural parasites, they hinder tree survival, growth and fecundity; hence, they negatively impact net ecosystem productivity and long-term carbon sequestration.Competition (for water and light) drives various forest processes and depends on the local abundance of resources over time. However, evaluating the relative role of resource availability on the interactions between lianas and trees from empirical observations is particularly challenging. Previous approaches have used labour-intensive and ecosystem-scale manipulation experiments, which are infeasible in most situations.We propose to circumvent this challenge by evaluating the uncertainty of water and light capture processes of a process-based vegetation model (ED2) including the liana growth form. We further developed the liana plant functional type in ED2 to mechanistically simulate water uptake and transport from roots to leaves, and start the model from prescribed initial conditions. We then used the PEcAn bioinformatics platform to constrain liana parameters and run uncertainty analyses.Baseline runs successfully reproduced ecosystem gas exchange fluxes (gross primary productivity and latent heat) and forest structural features (leaf area index, aboveground biomass) in two sites (Barro Colorado Island, Panama and Paracou, French Guiana) characterized by different rainfall regimes and levels of liana abundance.Model uncertainty analyses revealed that water limitation was the factor driving the competition between trees and lianas at the drier site (BCI), and during the relatively short dry season of the wetter site (Paracou). In young patches, light competition dominated in Paracou but alternated with water competition between the wet and the dry season on BCI according to the model simulations.The modelling workflow also identified key liana traits (photosynthetic quantum efficiency, stomatal regulation parameters, allometric relationships) and processes (water use, respiration, climbing) driving the model uncertainty. They should be considered as priorities for future data acquisition and model development to improve predictions of the carbon dynamics of liana-infested forests. Synthesis. Competition for water plays a larger role in the interaction between lianas and trees than previously hypothesized, as demonstrated by simulations from a process-based vegetation model.

Simulating Growth and Competition on Wet and Waterlogged Soils in a Forest Landscape Model

Changes in CO2 concentration and climate are likely to alter disturbance regimes and competitive outcomes among tree species, which ultimately can result in shifts of species and biome boundaries. Such changes are already evident in high latitude forests, where waterlogged soils produced by topography, surficial geology, and permafrost are an important driver of forest dynamics. Predicting such effects under the novel conditions of the future requires models with direct and mechanistic links of abiotic drivers to growth and competition. We enhanced such a forest landscape model (PnET-Succession in LANDIS-II) to allow simulation of waterlogged soils and their effects on tree growth and competition. We formally tested how these modifications alter water balance on wetland and permafrost sites, and their effect on tree growth and competition. We applied the model to evaluate its promise for mechanistically simulating species range expansion and contraction under climate change across a latitudinal gradient in Siberian Russia. We found that higher emissions scenarios permitted range expansions that were quicker and allowed a greater diversity of invading species, especially at the highest latitudes, and that disturbance hastened range shifts by overcoming the natural inertia of established ecological communities. The primary driver of range advances to the north was altered hydrology related to thawing permafrost, followed by temperature effects on growth. Range contractions from the south (extirpations) were slower and less tied to emissions or latitude, and were driven by inability to compete with invaders, or disturbance. An important non-intuitive result was that some extant species were killed off by extreme cold events projected under climate change as greater weather extremes occurred over the next 30 years, and this had important effects on subsequent successional trajectories. The mechanistic linkages between climate and soil water dynamics in this forest landscape model produced tight links between climate inputs, physiology of vegetation, and soils at a monthly time step. The updated modeling system can produce high quality projections of climate impacts on forest species range shifts by accounting for the interacting effects of CO2 concentration, climate (including longer growing seasons), seed dispersal, disturbance, and soil hydrologic properties.

Structure and parameter uncertainty in centennial projections of forest community structure and carbon cycling

Secondary forest regrowth shapes community succession and biogeochemistry for decades, including in the Upper Great Lakes region. Vegetation models encapsulate our understanding of forest function, and whether models can reproduce multi-decadal succession patterns is an indication of our ability to predict forest responses to future change. We test the ability of a vegetation model to simulate C cycling and community composition during 100 years of forest regrowth following stand-replacing disturbance, asking (a) Which processes and parameters are most important to accurately model Upper Midwest forest succession? (b) What is the relative importance of model structure versus parameter values to these predictions? We ran ensembles of the Ecosystem Demography model v2.2 with different representations of processes important to competition for light. We compared the magnitude of structural and parameter uncertainty and assessed which sub-model-parameter combinations best reproduced observed C fluxes and community composition. On average, our simulations underestimated observed net primary productivity (NPP) and leaf area index (LAI) after 100 years and predicted complete dominance by a single plant functional type (PFT). Out of 4,000 simulations, only nine fell within the observed range of both NPP and LAI, but these predicted unrealistically complete dominance by either early hardwood or pine PFTs. A different set of seven simulations were ecologically plausible but under-predicted observed NPP and LAI. Parameter uncertainty was large; NPP and LAI ranged from ~0% to >200% of their mean value, and any PFT could become dominant. The two parameters that contributed most to uncertainty in predicted NPP were plant-soil water conductance and growth respiration, both unobservable empirical coefficients. We conclude that (a) parameter uncertainty is more important than structural uncertainty, at least for ED-2.2 in Upper Midwest forests and (b) simulating both productivity and plant community composition accurately without physically unrealistic parameters remains challenging for demographic vegetation models.

Forest responses to simulated elevated CO2 under alternate hypotheses of size- and age-dependent mortality

Elevated atmospheric carbon dioxide (eCO₂ ) is predicted to increase growth rates of forest trees. The extent to which increased growth translates to changes in biomass is dependent on the turnover time of the carbon, and thus tree mortality rates. Size- or age-dependent mortality combined with increased growth rates could result in either decreased carbon turnover from a speeding up of tree life cycles, or increased biomass from trees reaching larger sizes, respectively. However, most vegetation models currently lack any representation of size- or age-dependent mortality and the effect of eCO₂ on changes in biomass and carbon turnover times is thus a major source of uncertainty in predictions of future vegetation dynamics. Using a reduced-complexity form of the vegetation demographic model the Functionally Assembled Terrestrial Ecosystem Simulator to simulate an idealised tropical forest, we find increases in biomass despite reductions in carbon turnover time in both size- and age-dependent mortality scenarios in response to a hypothetical eCO₂ -driven 25% increase in woody net primary productivity (wNPP). Carbon turnover times decreased by 9.6% in size-dependent mortality scenarios due to a speeding up of tree life cycles, but also by 2.0% when mortality was age-dependent, as larger crowns led to increased light competition. Increases in aboveground biomass (AGB) were much larger when mortality was age-dependent (24.3%) compared with size-dependent (13.4%) as trees reached larger sizes before death. In simulations with a constant background mortality rate, carbon turnover time decreased by 2.1% and AGB increased by 24.0%, however, absolute values of AGB and carbon turnover were higher than in either size- or age-dependent mortality scenario. The extent to which AGB increases and carbon turnover decreases will thus depend on the mechanisms of large tree mortality: if increased size itself results in elevated mortality rates, then this could reduce by about half the increase in AGB relative to the increase in wNPP.

Modeling the impact of liana infestation on the demography and carbon cycle of tropical forests

There is mounting empirical evidence that lianas affect the carbon cycle of tropical forests. However, no single vegetation model takes into account this growth form, although such efforts could greatly improve the predictions of carbon dynamics in tropical forests. In this study, we incorporated a novel mechanistic representation of lianas in a dynamic global vegetation model (the Ecosystem Demography Model). We developed a liana-specific plant functional type and mechanisms representing liana-tree interactions (such as light competition, liana-specific allometries, and attachment to host trees) and parameterized them according to a comprehensive literature meta-analysis. We tested the model for an old-growth forest (Paracou, French Guiana) and a secondary forest (Gigante Peninsula, Panama). The resulting model simulations captured many features of the two forests characterized by different levels of liana infestation as revealed by a systematic comparison of the model outputs with empirical data, including local census data from forest inventories, eddy flux tower data, and terrestrial laser scanner-derived forest vertical structure. The inclusion of lianas in the simulations reduced the secondary forest net productivity by up to 0.46 t_C  ha^-1  year^-1 , which corresponds to a limited relative reduction of 2.6% in comparison with a reference simulation without lianas. However, this resulted in significantly reduced accumulated above-ground biomass after 70 years of regrowth by up to 20 t_C /ha (19% of the reference simulation). Ultimately, the simulated negative impact of lianas on the total biomass was almost completely cancelled out when the forest reached an old-growth successional stage. Our findings suggest that lianas negatively influence the forest potential carbon sink strength, especially for young, disturbed, liana-rich sites. In light of the critical role that lianas play in the profound changes currently experienced by tropical forests, this new model provides a robust numerical tool to forecast the impact of lianas on tropical forest carbon sinks.

A dynamic yet vulnerable pipeline: Integration and coordination of hydraulic traits across whole plants

The vast majority of measurements in the field of plant hydraulics have been on small-diameter branches from woody species. These measurements have provided considerable insight into plant functioning, but our understanding of plant physiology and ecology would benefit from a broader view, because branch hydraulic properties are influenced by many factors. Here, we discuss the influence that other components of the hydraulic network have on branch vulnerability to embolism propagation. We also modelled the impact of changes in the ratio of root-to-leaf areas and soil texture on vulnerability to hydraulic failure along the soil-to-leaf continuum and showed that hydraulic function is better maintained through changes in root vulnerability and root-to-leaf area ratio than in branch vulnerability. Differences among species in the stringency with which they regulate leaf water potential and in reliance on stored water to buffer changes in water potential also affect the need to construct embolism resistant branches. Many approaches, such as measurements on fine roots, small individuals, combining sap flow and psychrometry techniques, and modelling efforts, could vastly improve our understanding of whole-plant hydraulic functioning. A better understanding of how traits are coordinated across the whole plant will improve predictions for plant function under future climate conditions.

Beyond Static Benchmarking: Using Experimental Manipulations to Evaluate Land Model Assumptions

Land models are often used to simulate terrestrial responses to future environmental changes, but these models are not commonly evaluated with data from experimental manipulations. Results from experimental manipulations can identify and evaluate model assumptions that are consistent with appropriate ecosystem responses to future environmental change. We conducted simulations using three coupled carbon-nitrogen versions of the Community Land Model (CLM, versions 4, 4.5, and-the newly developed-5), and compared the simulated response to nitrogen (N) and atmospheric carbon dioxide (CO2) enrichment with meta-analyses of observations from similar experimental manipulations. In control simulations, successive versions of CLM showed a poleward increase in gross primary productivity and an overall bias reduction, compared to FLUXNET-MTE observations. Simulations with N and CO2 enrichment demonstrate that CLM transitioned from a model that exhibited strong nitrogen limitation of the terrestrial carbon cycle (CLM4) to a model that showed greater responsiveness to elevated concentrations of CO2 in the atmosphere (CLM5). Overall, CLM5 simulations showed better agreement with observed ecosystem responses to experimental N and CO2 enrichment than previous versions of the model. These simulations also exposed shortcomings in structural assumptions and parameterizations. Specifically, no version of CLM captures changes in plant physiology, allocation, and nutrient uptake that are likely important aspects of terrestrial ecosystems' responses to environmental change. These highlight priority areas that should be addressed in future model developments. Moving forward, incorporating results from experimental manipulations into model benchmarking tools that are used to evaluate model performance will help increase confidence in terrestrial carbon cycle projections.

Wetland Biomass and Productivity in Coastal Louisiana: Base Line Data (1976–2015) and Knowledge Gaps for the Development of Spatially Explicit Models for Ecosystem Restoration and Rehabilitation Initiatives

Coastal Louisiana hosts 37% of the coastal wetland area in the conterminous US, including one of the deltaic coastal regions more susceptible to the synergy of human and natural impacts causing wetland loss. As a result of the construction of flood protection infrastructure, dredging of channels across wetlands for oil/gas exploration and maritime transport activities, coastal Louisiana has lost approximately 4900 km2 of wetland area since the early 1930s. Despite the economic relevance of both wetland biomass and net primary productivity (NPP) as ecosystem services, there is a lack of vegetation simulation models to forecast the trends of those functional attributes at the landscape level as hydrological restoration projects are implemented. Here, we review the availability of peer-reviewed biomass and NPP wetland data (below and aboveground) published during the period 1976–2015 for use in the development, calibration and validation of high spatial resolution (<200 m × 200 m) vegetation process-based ecological models. We discuss and list the knowledge gaps for those species that represent vegetation community associations of ecological importance, including the long-term research issues associated to limited number of paired belowground biomass and productivity studies across hydrological basins currently undergoing different freshwater diversions management regimes and hydrological restoration priorities.

Homoeostatic maintenance of nonstructural carbohydrates during the 2015-2016 El Niño drought across a tropical forest precipitation gradient

Nonstructural carbohydrates (NSCs) are essential for maintenance of plant metabolism and may be sensitive to short- and long-term climatic variation. NSC variation in moist tropical forests has rarely been studied, so regulation of NSCs in these systems is poorly understood. We measured foliar and branch NSC content in 23 tree species at three sites located across a large precipitation gradient in Panama during the 2015-2016 El Niño to examine how short- and long-term climatic variation impact carbohydrate dynamics. There was no significant difference in total NSCs as the drought progressed (leaf P = 0.32, branch P = 0.30) nor across the rainfall gradient (leaf P = 0.91, branch P = 0.96). Foliar soluble sugars decreased while starch increased over the duration of the dry period, suggesting greater partitioning of NSCs to storage than metabolism or transport as drought progressed. There was a large variation across species at all sites, but total foliar NSCs were positively correlated with leaf mass per area, whereas branch sugars were positively related to leaf temperature and negatively correlated with daily photosynthesis and wood density. The NSC homoeostasis across a wide range of conditions suggests that NSCs are an allocation priority in moist tropical forests.

Modelling water fluxes in plants: from tissues to biosphere

Contents Summary 1207 I. Introduction 1207 II. A brief history of modelling plant water fluxes 1208 III. Main components of plant water transport models 1208 IV. Stand-scale water fluxes and coupling to climate and soil 1213 V. Water fluxes in terrestrial biosphere models and feedbacks to community dynamics 1215 VI. Outstanding challenges in modelling water fluxes in the soil-plant-atmosphere continuum 1217 Acknowledgements 1218 References 1218 SUMMARY: Models of plant water fluxes have evolved from studies focussed on understanding the detailed structure and functioning of specific components of the soil-plant-atmosphere (SPA) continuum to architectures often incorporated inside eco-hydrological and terrestrial biosphere (TB) model schemes. We review here the historical evolution of this field, examine the basic structure of a simplified individual-based model of plant water transport, highlight selected applications for specific ecological problems and conclude by examining outstanding issues requiring further improvements in modelling vegetation water fluxes. We particularly emphasise issues related to the scaling from tissue-level traits to individual-based predictions of water transport, the representation of nonlinear and hysteretic behaviour in soil-xylem hydraulics and the need to incorporate knowledge of hydraulics within broader frameworks of plant ecological strategies and their consequences for predicting community demography and dynamics.

A Forest Model Intercomparison Framework and Application at Two Temperate Forests Along the East Coast of the United States

State-of-the-art forest models are often complex, analytically intractable, and computationally expensive, due to the explicit representation of detailed biogeochemical and ecological processes. Different models often produce distinct results while predictions from the same model vary with parameter values. In this project, we developed a rigorous quantitative approach for conducting model intercomparisons and assessing model performance. We have applied our original methodology to compare two forest biogeochemistry models, the Perfect Plasticity Approximation with Simple Biogeochemistry (PPA-SiBGC) and Landscape Disturbance and Succession with Net Ecosystem Carbon and Nitrogen (LANDIS-II NECN). We simulated past-decade conditions at flux tower sites located within Harvard Forest, MA, USA (HF-EMS) and Jones Ecological Research Center, GA, USA (JERC-RD). We mined field data available from both sites to perform model parameterization, validation, and intercomparison. We assessed model performance using the following time-series metrics: Net ecosystem exchange, aboveground net primary production, aboveground biomass, C, and N, belowground biomass, C, and N, soil respiration, and species total biomass and relative abundance. We also assessed static observations of soil organic C and N, and concluded with an assessment of general model usability, performance, and transferability. Despite substantial differences in design, both models achieved good accuracy across the range of pool metrics. While LANDIS-II NECN showed better fidelity to interannual NEE fluxes, PPA-SiBGC indicated better overall performance for both sites across the 11 temporal and two static metrics tested (HF-EMS R 2 ¯ = 0.73 , + 0.07 , R M S E ¯ = 4.68 , − 9.96 ; JERC-RD R 2 ¯ = 0.73 , + 0.01 , R M S E ¯ = 2.18 , − 1.64 ). To facilitate further testing of forest models at the two sites, we provide pre-processed datasets and original software written in the R language of statistical computing. In addition to model intercomparisons, our approach may be employed to test modifications to forest models and their sensitivity to different parameterizations.

Drivers and mechanisms of tree mortality in moist tropical forests

Tree mortality rates appear to be increasing in moist tropical forests (MTFs) with significant carbon cycle consequences. Here, we review the state of knowledge regarding MTF tree mortality, create a conceptual framework with testable hypotheses regarding the drivers, mechanisms and interactions that may underlie increasing MTF mortality rates, and identify the next steps for improved understanding and reduced prediction. Increasing mortality rates are associated with rising temperature and vapor pressure deficit, liana abundance, drought, wind events, fire and, possibly, CO₂ fertilization-induced increases in stand thinning or acceleration of trees reaching larger, more vulnerable heights. The majority of these mortality drivers may kill trees in part through carbon starvation and hydraulic failure. The relative importance of each driver is unknown. High species diversity may buffer MTFs against large-scale mortality events, but recent and expected trends in mortality drivers give reason for concern regarding increasing mortality within MTFs. Models of tropical tree mortality are advancing the representation of hydraulics, carbon and demography, but require more empirical knowledge regarding the most common drivers and their subsequent mechanisms. We outline critical datasets and model developments required to test hypotheses regarding the underlying causes of increasing MTF mortality rates, and improve prediction of future mortality under climate change.

Ecosystem heterogeneity and diversity mitigate Amazon forest resilience to frequent extreme droughts

The impact of increases in drought frequency on the Amazon forest's composition, structure and functioning remain uncertain. We used a process- and individual-based ecosystem model (ED2) to quantify the forest's vulnerability to increased drought recurrence. We generated meteorologically realistic, drier-than-observed rainfall scenarios for two Amazon forest sites, Paracou (wetter) and Tapajós (drier), to evaluate the impacts of more frequent droughts on forest biomass, structure and composition. The wet site was insensitive to the tested scenarios, whereas at the dry site biomass declined when average rainfall reduction exceeded 15%, due to high mortality of large-sized evergreen trees. Biomass losses persisted when year-long drought recurrence was shorter than 2-7 yr, depending upon soil texture and leaf phenology. From the site-level scenario results, we developed regionally applicable metrics to quantify the Amazon forest's climatological proximity to rainfall regimes likely to cause biomass loss > 20% in 50 yr according to ED2 predictions. Nearly 25% (1.8 million km² ) of the Amazon forests could experience frequent droughts and biomass loss if mean annual rainfall or interannual variability changed by 2σ. At least 10% of the high-emission climate projections (CMIP5/RCP8.5 models) predict critically dry regimes over 25% of the Amazon forest area by 2100.

Impacts of long-term precipitation manipulation on hydraulic architecture and xylem anatomy of piñon and juniper in Southwest USA

Hydraulic architecture imposes a fundamental control on water transport, underpinning plant productivity, and survival. The extent to which hydraulic architecture of mature trees acclimates to chronic drought is poorly understood, limiting accuracy in predictions of forest responses to future droughts. We measured seasonal shoot hydraulic performance for multiple years to assess xylem acclimation in mature piñon (Pinus edulis) and juniper (Juniperus monosperma) after 3+ years of precipitation manipulation. Our treatments consisted of water addition (+20% ambient precipitation), partial precipitation-exclusion (-45% ambient precipitation), and exclusion-structure control. Supplemental watering elevated leaf water potential, sapwood-area specific hydraulic conductivity, and leaf-area specific hydraulic conductivity relative to precipitation exclusion. Shifts in allocation of leaf area to sapwood area enhanced differences between irrigated and droughted K_L in piñon but not juniper. Piñon and juniper achieved similar K_L under ambient conditions, but juniper matched or outperformed piñon in all physiological measurements under both increased and decreased precipitation treatments. Embolism vulnerability and xylem anatomy were unaffected by treatments in either species. Absence of significant acclimation combined with inferior performance for both hydraulic transport and safety suggests piñon has greater risk of local extirpation if aridity increases as predicted in the southwestern USA.

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.

New insights into the covariation of stomatal, mesophyll and hydraulic conductances from optimization models incorporating nonstomatal limitations to photosynthesis

Optimization models of stomatal conductance (g_s ) attempt to explain observed stomatal behaviour in terms of cost--benefit tradeoffs. While the benefit of stomatal opening through increased CO₂ uptake is clear, currently the nature of the associated cost(s) remains unclear. We explored the hypothesis that g_s maximizes leaf photosynthesis, where the cost of stomatal opening arises from nonstomatal reductions in photosynthesis induced by leaf water stress. We analytically solved two cases, CAP and MES, in which reduced leaf water potential leads to reductions in carboxylation capacity (CAP) and mesophyll conductance (g_m ) (MES). Both CAP and MES predict the same one-parameter relationship between the intercellular : atmospheric CO₂ concentration ratio (c_i /c_a ) and vapour pressure deficit (VPD, D), viz. c_i /c_a  ≈ ξ/(ξ + √D), as that obtained from previous optimization models, with the novel feature that the parameter ξ is determined unambiguously as a function of a small number of photosynthetic and hydraulic variables. These include soil-to-leaf hydraulic conductance, implying a stomatal closure response to drought. MES also predicts that g_s /g_m is closely related to c_i /c_a and is similarly conservative. These results are consistent with observations, give rise to new testable predictions, and offer new insights into the covariation of stomatal, mesophyll and hydraulic conductances.

Predicting the abundance of forest types across the eastern United States through inverse modelling of tree demography

Global environmental change is expected to induce widespread changes in the geographic distribution and biomass of forest communities. Impacts have been projected from both empirical (statistical) and mechanistic (physiology-based) modelling approaches, but there remains an important gap in accurately predicting abundance across species' ranges from spatial variation in individual-level demographic processes. We address this issue by using a cohort-based forest dynamics model (CAIN) to predict spatial variation in the abundance of six plant functional types (PFTs) across the eastern United States. The model simulates tree-level growth, mortality, and recruitment, which we parameterized from data on both individual-level demographic rates and population-level abundance using Bayesian inverse modelling. Across a set of 1° grid cells, we calibrated local growth, mortality, and recruitment rates for each PFT to obtain a close match between predicted age-specific PFT basal area in forest stands and that observed in 46,603 Forest Inventory and Analysis plots. The resulting models produced a strong fit to PFT basal area across the region (R²  = 0.66-0.87), captured successional changes in PFT composition with stand age, and predicted the overall stem diameter distribution well. The mortality rates needed to accurately predict basal area were consistently higher than observed mortality, possibly because sampling effects led to biased individual-level mortality estimates across spatially heterogeneous plots. Growth and recruitment rates did not show consistent directional changes from observed values. Relative basal area was most strongly influenced by recruitment processes, but the effects of growth and mortality tended to increase as stands matured. Our study illustrates how both top-down (population-level) and bottom-up (individual-level) data can be combined to predict variation in abundance from size, environmental, and competitive effects on tree demography. Evidence for how demographic processes influence variation in abundance, as provided by our model, can help in understanding how these forests may respond to future environmental change.

Ecosystem functioning is enveloped by hydrometeorological variability

Terrestrial ecosystem processes, and the associated vegetation carbon dynamics, respond differently to hydrometeorological variability across timescales, and so does our scientific understanding of the underlying mechanisms. Long-term variability of the terrestrial carbon cycle is not yet well constrained and the resulting climate-biosphere feedbacks are highly uncertain. Here we present a comprehensive overview of hydrometeorological and ecosystem variability from hourly to decadal timescales integrating multiple in situ and remote-sensing datasets characterizing extra-tropical forest sites. We find that ecosystem variability at all sites is confined within a hydrometeorological envelope across sites and timescales. Furthermore, ecosystem variability demonstrates long-term persistence, highlighting ecological memory and slow ecosystem recovery rates after disturbances. However, simulation results with state-of-the-art process-based models do not reflect this long-term persistent behaviour in ecosystem functioning. Accordingly, we develop a cross-time-scale stochastic framework that captures hydrometeorological and ecosystem variability. Our analysis offers a perspective for terrestrial ecosystem modelling and paves the way for new model-data integration opportunities in Earth system sciences.

Trait-based representation of hydrological functional properties of plants in weather and ecosystem models

Land surface models and dynamic global vegetation models typically represent vegetation through coarse plant functional type groupings based on leaf form, phenology, and bioclimatic limits. Although these groupings were both feasible and functional for early model generations, in light of the pace at which our knowledge of functional ecology, ecosystem demographics, and vegetation-climate feedbacks has advanced and the ever growing demand for enhanced model performance, these groupings have become antiquated and are identified as a key source of model uncertainty. The newest wave of model development is centered on shifting the vegetation paradigm away from plant functional types (PFTs) and towards flexible trait-based representations. These models seek to improve errors in ecosystem fluxes that result from information loss due to over-aggregation of dissimilar species into the same functional class. We advocate the importance of the inclusion of plant hydraulic trait representation within the new paradigm through a framework of the whole-plant hydraulic strategy. Plant hydraulic strategy is known to play a critical role in the regulation of stomatal conductance and thus transpiration and latent heat flux. It is typical that coexisting plants employ opposing hydraulic strategies, and therefore have disparate patterns of water acquisition and use. Hydraulic traits are deterministic of drought resilience, response to disturbance, and other demographic processes. The addition of plant hydraulic properties in models may not only improve the simulation of carbon and water fluxes but also vegetation population distributions.

Recent climate hiatus revealed dual control by temperature and drought on the stem growth of Mediterranean Quercus ilex

A better understanding of stem growth phenology and its climate drivers would improve projections of the impact of climate change on forest productivity. Under a Mediterranean climate, tree growth is primarily limited by soil water availability during summer, but cold temperatures in winter also prevent tree growth in evergreen forests. In the widespread Mediterranean evergreen tree species Quercus ilex, the duration of stem growth has been shown to predict annual stem increment, and to be limited by winter temperatures on the one hand, and by the summer drought onset on the other hand. We tested how these climatic controls of Q. ilex growth varied with recent climate change by correlating a 40-year tree ring record and a 30-year annual diameter inventory against winter temperature, spring precipitation, and simulated growth duration. Our results showed that growth duration was the best predictor of annual tree growth. We predicted that recent climate changes have resulted in earlier growth onset (-10 days) due to winter warming and earlier growth cessation (-26 days) due to earlier drought onset. These climatic trends partly offset one another, as we observed no significant trend of change in tree growth between 1968 and 2008. A moving-window correlation analysis revealed that in the past, Q. ilex growth was only correlated with water availability, but that since the 2000s, growth suddenly became correlated with winter temperature in addition to spring drought. This change in the climate-growth correlations matches the start of the recent atmospheric warming pause also known as the 'climate hiatus'. The duration of growth of Q. ilex is thus shortened because winter warming has stopped compensating for increasing drought in the last decade. Decoupled trends in precipitation and temperature, a neglected aspect of climate change, might reduce forest productivity through phenological constraints and have more consequences than climate warming alone.

Plant hydraulics improves and topography mediates prediction of aspen mortality in southwestern USA

Elevated forest mortality has been attributed to climate change-induced droughts, but prediction of spatial mortality patterns remains challenging. We evaluated whether introducing plant hydraulics and topographic convergence-induced soil moisture variation to land surface models (LSM) can help explain spatial patterns of mortality. A scheme predicting plant hydraulic safety loss from soil moisture was developed using field measurements and a plant physiology-hydraulics model, TREES. The scheme was upscaled to Populus tremuloides forests across Colorado, USA, using LSM-modeled and topography-mediated soil moisture, respectively. The spatial patterns of hydraulic safety loss were compared against aerial surveyed mortality. Incorporating hydraulic safety loss raised the explanatory power of mortality by 40% compared to LSM-modeled soil moisture. Topographic convergence was mostly influential in suppressing mortality in low and concave areas, explaining an additional 10% of the variations in mortality for those regions. Plant hydraulics integrated water stress along the soil-plant continuum and was more closely tied to plant physiological response to drought. In addition to the well-recognized topo-climate influence due to elevation and aspect, we found evidence that topographic convergence mediates tree mortality in certain parts of the landscape that are low and convergent, likely through influences on plant-available water.

Variation in stem mortality rates determines patterns of above-ground biomass in Amazonian forests: implications for dynamic global vegetation models

Understanding the processes that determine above-ground biomass (AGB) in Amazonian forests is important for predicting the sensitivity of these ecosystems to environmental change and for designing and evaluating dynamic global vegetation models (DGVMs). AGB is determined by inputs from woody productivity [woody net primary productivity (NPP)] and the rate at which carbon is lost through tree mortality. Here, we test whether two direct metrics of tree mortality (the absolute rate of woody biomass loss and the rate of stem mortality) and/or woody NPP, control variation in AGB among 167 plots in intact forest across Amazonia. We then compare these relationships and the observed variation in AGB and woody NPP with the predictions of four DGVMs. The observations show that stem mortality rates, rather than absolute rates of woody biomass loss, are the most important predictor of AGB, which is consistent with the importance of stand size structure for determining spatial variation in AGB. The relationship between stem mortality rates and AGB varies among different regions of Amazonia, indicating that variation in wood density and height/diameter relationships also influences AGB. In contrast to previous findings, we find that woody NPP is not correlated with stem mortality rates and is weakly positively correlated with AGB. Across the four models, basin-wide average AGB is similar to the mean of the observations. However, the models consistently overestimate woody NPP and poorly represent the spatial patterns of both AGB and woody NPP estimated using plot data. In marked contrast to the observations, DGVMs typically show strong positive relationships between woody NPP and AGB. Resolving these differences will require incorporating forest size structure, mechanistic models of stem mortality and variation in functional composition in DGVMs.

Key knowledge and data gaps in modelling the influence of CO2 concentration on the terrestrial carbon sink

Primary productivity of terrestrial vegetation is expected to increase under the influence of increasing atmospheric carbon dioxide concentrations ([CO₂]). Depending on the fate of such additionally fixed carbon, this could lead to an increase in terrestrial carbon storage, and thus a net terrestrial sink of atmospheric carbon. Such a mechanism is generally believed to be the primary global driver behind the observed large net uptake of anthropogenic CO₂ emissions by the biosphere. Mechanisms driving CO₂ uptake in the Terrestrial Biosphere Models (TBMs) used to attribute and project terrestrial carbon sinks, including that from increased [CO₂], remain in large parts unchanged since those models were conceived two decades ago. However, there exists a large body of new data and understanding providing an opportunity to update these models, and directing towards important topics for further research. In this review we highlight recent developments in understanding of the effects of elevated [CO₂] on photosynthesis, and in particular on the fate of additionally fixed carbon within the plant with its implications for carbon turnover rates, on the regulation of photosynthesis in response to environmental limitations on in-plant carbon sinks, and on emergent ecosystem responses. We recommend possible avenues for model improvement and identify requirements for better data on core processes relevant to the understanding and modelling of the effect of increasing [CO₂] on the global terrestrial carbon sink.

Evolution and challenges of dynamic global vegetation models for some aspects of plant physiology and elevated atmospheric CO2

Dynamic global vegetation models (DGVMs) simulate surface processes such as the transfer of energy, water, CO2, and momentum between the terrestrial surface and the atmosphere, biogeochemical cycles, carbon assimilation by vegetation, phenology, and land use change in scenarios of varying atmospheric CO2 concentrations. DGVMs increase the complexity and the Earth system representation when they are coupled with atmospheric global circulation models (AGCMs) or climate models. However, plant physiological processes are still a major source of uncertainty in DGVMs. The maximum velocity of carboxylation (Vcmax), for example, has a direct impact over productivity in the models. This parameter is often underestimated or imprecisely defined for the various plant functional types (PFTs) and ecosystems. Vcmax is directly related to photosynthesis acclimation (loss of response to elevated CO2), a widely known phenomenon that usually occurs when plants are subjected to elevated atmospheric CO2 and might affect productivity estimation in DGVMs. Despite this, current models have improved substantially, compared to earlier models which had a rudimentary and very simple representation of vegetation-atmosphere interactions. In this paper, we describe this evolution through generations of models and the main events that contributed to their improvements until the current state-of-the-art class of models. Also, we describe some main challenges for further improvements to DGVMs.

Tree mortality from an exceptional drought spanning mesic to semiarid ecoregions

Significant areas of the southern USA periodically experience intense drought that can lead to episodic tree mortality events. Because drought tolerance varies among species and size of trees, such events can alter the structure and function of terrestrial ecosystem in ways that are difficult to detect with local data sets or solely with remote-sensing platforms. We investigated a widespread tree mortality event that resulted from the worst 1-year drought on record for the state of Texas, USA. The drought affected ecoregions spanning mesic to semiarid climate zones and provided a unique opportunity to test hypotheses related to how trees of varying genus and size were affected. The study was based on an extensive set of 599 distributed plots, each 0.16 ha, surveyed in the summer following the drought. In each plot, dead trees larger than 12.7 cm in diameter were counted, sized, and identified to the genus level. Estimates of total mortality were obtained for each of 10 regions using a combination of design-based estimators and calibrated remote sensing using MODIS 1-yr change in normalized difference vegetation index products developed by the U.S. Forest Service. As compared with most of the publicized extreme die-off events, this study documents relatively low rates of mortality occurring over a very large area. However, statewide, regional tree mortality was massive, with an estimated 6.2% of the live trees perishing, nearly nine times greater than normal annual mortality. Dead tree diameters averaged larger than the live trees for most ecoregions, and this trend was most pronounced in the wetter climate zones, suggesting a potential re-ordering of species dominance and downward trend in tree size that was specific to climatic regions. The net effect on carbon storage was estimated to be a redistribution of 24-30 Tg C from the live tree to dead tree carbon pool. The dead tree survey documented drought mortality in more than 29 genera across all regions, and surprisingly, drought resistant and sensitive species fared similarly in some regions. Both angiosperms and gymnosperms were affected. These results highlight that drought-driven mortality alters forest structure differently across climatic regions and genera.

Quantifying and understanding reproductive allocation schedules in plants

A plant's reproductive allocation (RA) schedule describes the fraction of surplus energy allocated to reproduction as it increases in size. While theorists use RA schedules as the connection between life history and energy allocation, little is known about RA schedules in real vegetation. Here we review what is known about RA schedules for perennial plants using studies either directly quantifying RA or that collected data from which the shape of an RA schedule can be inferred. We also briefly review theoretical models describing factors by which variation in RA may arise. We identified 34 studies from which aspects of an RA schedule could be inferred. Within those, RA schedules varied considerably across species: some species abruptly shift all resources from growth to reproduction; most others gradually shift resources into reproduction, but under a variety of graded schedules. Available data indicate the maximum fraction of energy allocated to production ranges from 0.1 to 1 and that shorter lived species tend to have higher initial RA and increase their RA more quickly than do longer-lived species. Overall, our findings indicate, little data exist about RA schedules in perennial plants. Available data suggest a wide range of schedules across species. Collection of more data on RA schedules would enable a tighter integration between observation and a variety of models predicting optimal energy allocation, plant growth rates, and biogeochemical cycles.