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.
In this review, we summarize in detail the development of vegetation demographics models as components of Earth System Models. We particularly highlight the ecological uncertainties around the strength of growth‐resource acquisition feedbacks that are common across model developments, and illustrate the myriad new opportunities for ecological‐scale data streams to inform and validate these new model structures.
To predict forest response to long‐term climate change with high confidence requires that dynamic global vegetation models (DGVMs) be successfully tested against ecosystem response to short‐term ...variations in environmental drivers, including regular seasonal patterns. Here, we used an integrated dataset from four forests in the Brasil flux network, spanning a range of dry‐season intensities and lengths, to determine how well four state‐of‐the‐art models (IBIS, ED2, JULES, and CLM3.5) simulated the seasonality of carbon exchanges in Amazonian tropical forests. We found that most DGVMs poorly represented the annual cycle of gross primary productivity (GPP), of photosynthetic capacity (Pc), and of other fluxes and pools. Models simulated consistent dry‐season declines in GPP in the equatorial Amazon (Manaus K34, Santarem K67, and Caxiuanã CAX); a contrast to observed GPP increases. Model simulated dry‐season GPP reductions were driven by an external environmental factor, ‘soil water stress’ and consequently by a constant or decreasing photosynthetic infrastructure (Pc), while observed dry‐season GPP resulted from a combination of internal biological (leaf‐flush and abscission and increased Pc) and environmental (incoming radiation) causes. Moreover, we found models generally overestimated observed seasonal net ecosystem exchange (NEE) and respiration (Re) at equatorial locations. In contrast, a southern Amazon forest (Jarú RJA) exhibited dry‐season declines in GPP and Re consistent with most DGVMs simulations. While water limitation was represented in models and the primary driver of seasonal photosynthesis in southern Amazonia, changes in internal biophysical processes, light‐harvesting adaptations (e.g., variations in leaf area index (LAI) and increasing leaf‐level assimilation rate related to leaf demography), and allocation lags between leaf and wood, dominated equatorial Amazon carbon flux dynamics and were deficient or absent from current model formulations. Correctly simulating flux seasonality at tropical forests requires a greater understanding and the incorporation of internal biophysical mechanisms in future model developments.
Forest ecosystem models based on heuristic water stress functions poorly predict tropical forest response to drought partly because they do not capture the diversity of hydraulic traits (including ...variation in tree size) observed in tropical forests. We developed a continuous porous media approach to modeling plant hydraulics in which all parameters of the constitutive equations are biologically interpretable and measurable plant hydraulic traits (e.g., turgor loss point πtlp, bulk elastic modulus ε, hydraulic capacitance Cft, xylem hydraulic conductivity ks,max, water potential at 50 % loss of conductivity for both xylem (P50,x) and stomata (P50,gs), and the leaf : sapwood area ratio Al : As). We embedded this plant hydraulics model within a trait forest simulator (TFS) that models light environments of individual trees and their upper boundary conditions (transpiration), as well as providing a means for parameterizing variation in hydraulic traits among individuals. We synthesized literature and existing databases to parameterize all hydraulic traits as a function of stem and leaf traits, including wood density (WD), leaf mass per area (LMA), and photosynthetic capacity (Amax), and evaluated the coupled model (called TFS v.1-Hydro) predictions, against observed diurnal and seasonal variability in stem and leaf water potential as well as stand-scaled sap flux. Our hydraulic trait synthesis revealed coordination among leaf and xylem hydraulic traits and statistically significant relationships of most hydraulic traits with more easily measured plant traits. Using the most informative empirical trait–trait relationships derived from this synthesis, TFS v.1-Hydro successfully captured individual variation in leaf and stem water potential due to increasing tree size and light environment, with model representation of hydraulic architecture and plant traits exerting primary and secondary controls, respectively, on the fidelity of model predictions. The plant hydraulics model made substantial improvements to simulations of total ecosystem transpiration. Remaining uncertainties and limitations of the trait paradigm for plant hydraulics modeling are highlighted.
Considerable uncertainty surrounds the fate of Amazon rainforests in response to
climate change.
Here, carbon (C) flux predictions of five terrestrial biosphere models (Community
Land Model version ...3.5 (CLM3.5), Ecosystem Demography model version 2.1 (ED2),
Integrated BIosphere Simulator version 2.6.4 (IBIS), Joint UK Land Environment
Simulator version 2.1 (JULES) and Simple Biosphere model version 3 (SiB3)) and a
hydrodynamic terrestrial ecosystem model (the
Soil–Plant–Atmosphere (SPA) model) were evaluated against
measurements from two large-scale Amazon drought experiments.
Model predictions agreed with the observed C fluxes in the control plots of both
experiments, but poorly replicated the responses to the drought treatments. Most
notably, with the exception of ED2, the models predicted negligible reductions
in aboveground biomass in response to the drought treatments, which was in
contrast to an observed c. 20% reduction at both sites.
For ED2, the timing of the decline in aboveground biomass was accurate, but the
magnitude was too high for one site and too low for the other.
Three key findings indicate critical areas for future research and model
development. First, the models predicted declines in autotrophic respiration
under prolonged drought in contrast to measured increases at one of the sites.
Secondly, models lacking a phenological response to drought introduced bias in
the sensitivity of canopy productivity and respiration to drought. Thirdly, the
phenomenological water-stress functions used by the terrestrial biosphere models
to represent the effects of soil moisture on stomatal conductance yielded
unrealistic diurnal and seasonal responses to drought.
Gross ecosystem productivity (GEP) in tropical forests varies both with the environment and with biotic changes in photosynthetic infrastructure, but our understanding of the relative effects of ...these factors across timescales is limited. Here, we used a statistical model to partition the variability of seven years of eddy covariance‐derived GEP in a central Amazon evergreen forest into two main causes: variation in environmental drivers (solar radiation, diffuse light fraction, and vapor pressure deficit) that interact with model parameters that govern photosynthesis and biotic variation in canopy photosynthetic light‐use efficiency associated with changes in the parameters themselves. Our fitted model was able to explain most of the variability in GEP at hourly (R2 = 0.77) to interannual (R2 = 0.80) timescales. At hourly timescales, we found that 75% of observed GEP variability could be attributed to environmental variability. When aggregating GEP to the longer timescales (daily, monthly, and yearly), however, environmental variation explained progressively less GEP variability: At monthly timescales, it explained only 3%, much less than biotic variation in canopy photosynthetic light‐use efficiency, which accounted for 63%. These results challenge modeling approaches that assume GEP is primarily controlled by the environment at both short and long timescales. Our approach distinguishing biotic from environmental variability can help to resolve debates about environmental limitations to tropical forest photosynthesis. For example, we found that biotically regulated canopy photosynthetic light‐use efficiency (associated with leaf phenology) increased with sunlight during dry seasons (consistent with light but not water limitation of canopy development) but that realized GEP was nonetheless lower relative to its potential efficiency during dry than wet seasons (consistent with water limitation of photosynthesis in given assemblages of leaves). This work highlights the importance of accounting for differential regulation of GEP at different timescales and of identifying the underlying feedbacks and adaptive mechanisms.
Droughts in a warming climate have become more common and more extreme, making understanding forest responses to water stress increasingly pressing. Analysis of water stress in trees has long focused ...on water potential in xylem and leaves, which influences stomatal closure and water flow through the soil‐plant‐atmosphere continuum. At the same time, changes of vegetation water content (VWC) are linked to a range of tree responses, including fluxes of water and carbon, mortality, flammability, and more. Unlike water potential, which requires demanding in situ measurements, VWC can be retrieved from remote sensing measurements, particularly at microwave frequencies using radar and radiometry. Here, we highlight key frontiers through which VWC has the potential to significantly increase our understanding of forest responses to water stress. To validate remote sensing observations of VWC at landscape scale and to better relate them to data assimilation model parameters, we introduce an ecosystem‐scale analog of the pressure–volume curve, the non‐linear relationship between average leaf or branch water potential and water content commonly used in plant hydraulics. The sources of variability in these ecosystem‐scale pressure‐volume curves and their relationship to forest response to water stress are discussed. We further show to what extent diel, seasonal, and decadal dynamics of VWC reflect variations in different processes relating the tree response to water stress. VWC can also be used for inferring belowground conditions—which are difficult to impossible to observe directly. Lastly, we discuss how a dedicated geostationary spaceborne observational system for VWC, when combined with existing datasets, can capture diel and seasonal water dynamics to advance the science and applications of global forest vulnerability to future droughts.
Changes of vegetation water content (VWC) are linked to a range of tree responses to drought, including fluxes of water and carbon, mortality, flammability, and more, and can be retrieved from microwave remote sensing measurements. We highlight key frontiers through which remotely sensed VWC has the potential to significantly increase our understanding of forest responses to water stress. We argue that separate consideration of diel, seasonal, and decadal timescales can facilitate interpretation of VWC measurements for different process studies, and that VWC observations can be useful for constraining belowground water uptake. To link remotely sensed VWC estimates to plant hydraulic models, the utility and interpretation of ecosystem‐scale pressure‐volume curves are discussed.
Understanding the effects of intensification of Amazon basin hydrological cycling—manifest as increasingly frequent floods and droughts—on water and energy cycles of tropical forests is essential to ...meeting the challenge of predicting ecosystem responses to climate change, including forest “tipping points”. Here, we investigated the impacts of hydrological extremes on forest function using 12+ years of observations (between 2001–2020) of water and energy fluxes from eddy covariance, along with associated ecological dynamics from biometry, at the Tapajós National Forest. Measurements encompass the strong 2015–2016 El Niño drought and La Niña 2008–2009 wet events. We found that the forest responded strongly to El Niño‐Southern Oscillation (ENSO): Drought reduced water availability for evapotranspiration (ET) leading to large increases in sensible heat fluxes (H). Partitioning ET by an approach that assumes transpiration (T) is proportional to photosynthesis, we found that water stress‐induced reductions in canopy conductance (Gs) drove T declines partly compensated by higher evaporation (E). By contrast, the abnormally wet La Niña period gave higher T and lower E, with little change in seasonal ET. Both El Niño‐Southern Oscillation (ENSO) events resulted in changes in forest structure, manifested as lower wet‐season leaf area index. However, only during El Niño 2015–2016, we observed a breakdown in the strong meteorological control of transpiration fluxes (via energy availability and atmospheric demand) because of slowing vegetation functions (via shutdown of Gs and significant leaf shedding). Drought‐reduced T and Gs, higher H and E, amplified by feedbacks with higher temperatures and vapor pressure deficits, signaled that forest function had crossed a threshold, from which it recovered slowly, with delay, post‐drought. Identifying such tipping point onsets (beyond which future irreversible processes may occur) at local scale is crucial for predicting basin‐scale threshold‐crossing changes in forest energy and water cycling, leading to slow‐down in forest function, potentially resulting in Amazon forests shifting into alternate degraded states.
We studied tropical forest's response to hydrological extremes—2015–2016 El Niño drought and 2008–2009 La Niña wet events—at the K67 Tapajós National Forest, by analyzing 12+ years of micrometeorological and biometric data.
During the wet La Niña, transpiration increased while evaporation decreased, with little change in seasonal water fluxes. Conversely, El Niño's drought led to the breakdown of the strong meteorological control over transpiration as vegetation functions slowed (leaf shedding increased and stomatal conductance shutdown). Reduced evapotranspiration resulted in higher sensible heat fluxes, amplified by feedback effects (higher temperatures and atmospheric demand). The forest crossed a threshold, recovering slowly post‐drought.
The role of disturbance in accelerating weed growth is well understood. While most studies have focused on soil mediated disturbance, mowing can also impact weed traits. Using silverleaf nightshade ...(Solanum elaeagnifolium), a noxious and invasive weed, through a series of field, laboratory, and greenhouse experiments, we asked whether continuous mowing influences growth and plant defense traits, expressed via different avenues, and whether they cascade into offspring. We found that mowed plants produced significantly less number of fruits, and less number of total seeds per plant, but had higher seed mass, and germinated more and faster. When three herbivores were allowed to feed, tobacco hornworm (Manduca sexta) caterpillars, gained more mass on seedlings from unmowed plants, while cow pea aphid (Aphis craccivora), a generalist, established better on mowed seedlings; however, leaf trichome density was higher on unmowed seedlings, suggesting possible negative cross talk in defense traits. Texas potato beetle (Leptinotarsa texana), a co-evolved specialist on S. elaeagnifolium, did not show any differential feeding effects. We also found that specific root length, an indicator of nutrient acquisition, was significantly higher in first generation seedlings from mowed plants. Taken together, we show that mowing is a selective pressure that enhances some fitness and defense traits and can contribute to producing superweeds.
Woody plants vary in their adaptations to drought and shade. For a better prediction of vegetation responses to drought and shade within dynamic global vegetation models, it is critical to group ...species into functional types with similar adaptations. One of the key challenges is that the adaptations are generally determined by a large number of plant traits that may not be available for a large number of species. In this study, we present two heuristic woody plant groups that were separated using cluster analysis in a three-dimensional trait-environment space based on three key metrics for each species: mean xylem embolism resistance, shade tolerance and habitat aridity. The two heuristic groups separate these species into tolerators and avoiders. The tolerators either rely on their high embolism resistance to tolerate drought in arid habitats (e.g., Juniperus and Prunus) or rely on high shade tolerance to withstand shaded conditions in wet habitats (e.g., Picea, Abies and Acer). In contrast, all avoiders have low embolism resistance and low shade tolerance. In arid habitats, avoiders tend to minimize catastrophic embolism (e.g., most Pinus species) while in wet habitats, they may survive despite low shade tolerance (e.g., Betula, Populus, Alnus and Salix). Because our approach links traits to the environmental conditions, we expect it could be a promising framework for predicting changes in species composition, and therefore ecosystem function, under changing environmental conditions.
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