Recent studies show coordinated relationships between plant leaf traits and their capacity to predict ecosystem functions. However, how leaf traits will change within species and whether ...interspecific trait relationships will shift under future environmental changes both remain unclear. Here, we examine the bivariate correlations between leaf economic traits of 515 species in 210 experiments which mimic climate warming, drought, elevated CO
, and nitrogen deposition. We find divergent directions of changes in trait-pairs between species, and the directions mostly do not follow the interspecific trait relationships. However, the slopes in the logarithmic transformed interspecific trait relationships hold stable under environmental changes, while only their elevations vary. The elevation changes of trait relationship are mainly driven by asymmetrically interspecific responses contrary to the direction of the leaf economic spectrum. These findings suggest robust interspecific trait relationships under global changes, and call for linking within-species responses to interspecific coordination of plant traits.
In this review, we propose a new framework, dynamic disequilibrium of the carbon cycles, to assess future land carbon-sink dynamics. The framework recognizes internal ecosystem processes that drive ...the carbon cycle toward equilibrium, such as donor pool-dominated transfer; and external forces that create disequilibrium, such as disturbances and global change. Dynamic disequilibrium within one disturbance–recovery episode causes temporal changes in the carbon source and sink at yearly and decadal scales, but has no impacts on longer-term carbon sequestration unless disturbance regimes shift. Such shifts can result in long-term regional carbon loss or gain and be quantified by stochastic statistics for use in prognostic modeling. If the regime shifts result in ecosystem state changes in regions with large carbon reserves at risk, the global carbon cycle might be destabilized.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UL, UM, UPCLJ, UPUK
We conducted a modeling study to evaluate how soil hydrological properties regulate water and carbon dynamics of grassland ecosystems in response to multifactor global change. We first calibrated a ...process‐based terrestrial ecosystem (TECO) model against data from two experiments with warming and clipping or doubled precipitation in Great Plains. The calibrated model was used to simulate responses of soil moisture, evaporation, transpiration, runoff, net primary production (NPP), ecosystem respiration (Rh), and net ecosystem production (NEP) to changes in precipitation amounts and intensity, increased temperature, and elevated atmospheric CO2 along a soil texture gradient (sand, sandy loam, loam, silt loam, and clay loam). Soil available water capacity (AWC), which is the difference between field capacity and wilting point, was used as the index to represent soil hydrological properties of the five soil texture types. Simulation results showed that soil AWC altered partitioning of precipitation among runoff, evaporation, and transpiration, and consequently regulated ecosystem responses to global environmental changes. The fractions of precipitation that were used for evaporation and transpiration increased with soil AWC but decreased for runoff. High AWC could greatly buffer water stress during long drought periods, particularly after a large rainfall event. NPP, Rh, and NEP usually increased with AWC under ambient and 50% increased precipitation scenarios. With the halved precipitation amount, NPP, Rh, and NEP only increased from 7% to 7.5% of AWC followed by declines. Warming and CO2 effects on soil moisture, evapotranspiration, and runoff were magnified by soil AWC. Regulatory patterns of AWC on responses of NPP, Rh, and NEP to warming were complex. In general, CO2 effects on NPP, Rh, and NEP increased with soil AWC. Our results indicate that variations in soil texture may be one of the major causes underlying variable responses of ecosystems to global changes observed from different experiments.
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.
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BFBNIB, FZAB, GIS, IJS, KILJ, NLZOH, NUK, OILJ, SAZU, SBCE, SBMB, UL, UM, UPUK
Terrestrial gross primary productivity (GPP) varies greatly over time and space. A better understanding of this variability is necessary for more accurate predictions of the future climate–carbon ...cycle feedback. Recent studies have suggested that variability in GPP is driven by a broad range of biotic and abiotic factors operating mainly through changes in vegetation phenology and physiological processes. However, it is still unclear how plant phenology and physiology can be integrated to explain the spatiotemporal variability of terrestrial GPP. Based on analyses of eddy–covariance and satellite-derived data, we decomposed annual terrestrial GPP into the length of the CO ₂ uptake period (CUP) and the seasonal maximal capacity of CO ₂ uptake (GPP ₘₐₓ). The product of CUP and GPP ₘₐₓ explained >90% of the temporal GPP variability in most areas of North America during 2000–2010 and the spatial GPP variation among globally distributed eddy flux tower sites. It also explained GPP response to the European heatwave in 2003 ( r ² = 0.90) and GPP recovery after a fire disturbance in South Dakota ( r ² = 0.88). Additional analysis of the eddy–covariance flux data shows that the interbiome variation in annual GPP is better explained by that in GPP ₘₐₓ than CUP. These findings indicate that terrestrial GPP is jointly controlled by ecosystem-level plant phenology and photosynthetic capacity, and greater understanding of GPP ₘₐₓ and CUP responses to environmental and biological variations will, thus, improve predictions of GPP over time and space.
Significance Terrestrial gross primary productivity (GPP), the total photosynthetic CO ₂ fixation at ecosystem level, fuels all life on land. However, its spatiotemporal variability is poorly understood, because GPP is determined by many processes related to plant phenology and physiological activities. In this study, we find that plant phenological and physiological properties can be integrated in a robust index—the product of the length of CO ₂ uptake period and the seasonal maximal photosynthesis—to explain the GPP variability over space and time in response to climate extremes and during recovery after disturbance.
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BFBNIB, NMLJ, NUK, PNG, SAZU, UL, UM, UPUK
Biogeochemical models have been used to evaluate long-term ecosystem responses to global change on decadal and century time scales. Recently, data assimilation has been applied to improve these ...models for ecological forecasting. It is not clear what the relative information contributions of model (structure and parameters) vs. data are to constraints of short- and long-term forecasting. In this study, we assimilated eight sets of 10-year data (foliage, woody, and fine root biomass, litter fall, forest floor carbon C, microbial C, soil C, and soil respiration) collected from Duke Forest into a Terrestrial Ecosystem model (TECO). The relative information contribution was measured by Shannon information index calculated from probability density functions (PDFs) of carbon pool sizes. The null knowledge without a model or data was defined by the uniform PDF within a prior range. The relative model contribution was information content in the PDF of modeled carbon pools minus that in the uniform PDF, while the relative data contribution was the information content in the PDF of modeled carbon pools after data was assimilated minus that before data assimilation. Our results showed that the information contribution of the model to constrain carbon dynamics increased with time whereas the data contribution declined. The eight data sets contributed more than the model to constrain C dynamics in foliage and fine root pools over the 100-year forecasts. The model, however, contributed more than the data sets to constrain the litter, fast soil organic matter (SOM), and passive SOM pools. For the two major C pools, woody biomass and slow SOM, the model contributed less information in the first few decades and then more in the following decades than the data. Knowledge of relative information contributions of model vs. data is useful for model development, uncertainty analysis, future data collection, and evaluation of ecological forecasting.
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BFBNIB, FZAB, GIS, IJS, INZLJ, KILJ, NLZOH, NMLJ, NUK, OILJ, PNG, SAZU, SBCE, SBMB, UL, UM, UPUK, ZRSKP
We analysed the responses of 11 ecosystem models to elevated atmospheric CO₂ (eCO₂) at two temperate forest ecosystems (Duke and Oak Ridge National Laboratory (ORNL) Free‐Air CO₂ Enrichment (FACE) ...experiments) to test alternative representations of carbon (C)–nitrogen (N) cycle processes. We decomposed the model responses into component processes affecting the response to eCO₂ and confronted these with observations from the FACE experiments. Most of the models reproduced the observed initial enhancement of net primary production (NPP) at both sites, but none was able to simulate both the sustained 10‐yr enhancement at Duke and the declining response at ORNL: models generally showed signs of progressive N limitation as a result of lower than observed plant N uptake. Nonetheless, many models showed qualitative agreement with observed component processes. The results suggest that improved representation of above‐ground–below‐ground interactions and better constraints on plant stoichiometry are important for a predictive understanding of eCO₂ effects. Improved accuracy of soil organic matter inventories is pivotal to reduce uncertainty in the observed C–N budgets. The two FACE experiments are insufficient to fully constrain terrestrial responses to eCO₂, given the complexity of factors leading to the observed diverging trends, and the consequential inability of the models to explain these trends. Nevertheless, the ecosystem models were able to capture important features of the experiments, lending some support to their projections.
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BFBNIB, FZAB, GIS, IJS, KILJ, NLZOH, NMLJ, NUK, OILJ, PNG, SAZU, SBCE, SBMB, UL, UM, UPUK
Elevated atmospheric CO₂ concentration (eCO₂) has the potential to increase vegetation carbon storage if increased net primary production causes increased long‐lived biomass. Model predictions of ...eCO₂ effects on vegetation carbon storage depend on how allocation and turnover processes are represented. We used data from two temperate forest free‐air CO₂ enrichment (FACE) experiments to evaluate representations of allocation and turnover in 11 ecosystem models. Observed eCO₂ effects on allocation were dynamic. Allocation schemes based on functional relationships among biomass fractions that vary with resource availability were best able to capture the general features of the observations. Allocation schemes based on constant fractions or resource limitations performed less well, with some models having unintended outcomes. Few models represent turnover processes mechanistically and there was wide variation in predictions of tissue lifespan. Consequently, models did not perform well at predicting eCO₂ effects on vegetation carbon storage. Our recommendations to reduce uncertainty include: use of allocation schemes constrained by biomass fractions; careful testing of allocation schemes; and synthesis of allocation and turnover data in terms of model parameters. Data from intensively studied ecosystem manipulation experiments are invaluable for constraining models and we recommend that such experiments should attempt to fully quantify carbon, water and nutrient budgets.
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BFBNIB, FZAB, GIS, IJS, KILJ, NLZOH, NMLJ, NUK, OILJ, PNG, SAZU, SBCE, SBMB, UL, UM, UPUK
Predicted responses of transpiration to elevated atmospheric CO2 concentration (eCO2) are highly variable amongst process‐based models. To better understand and constrain this variability amongst ...models, we conducted an intercomparison of 11 ecosystem models applied to data from two forest free‐air CO2 enrichment (FACE) experiments at Duke University and Oak Ridge National Laboratory. We analysed model structures to identify the key underlying assumptions causing differences in model predictions of transpiration and canopy water use efficiency. We then compared the models against data to identify model assumptions that are incorrect or are large sources of uncertainty. We found that model‐to‐model and model‐to‐observations differences resulted from four key sets of assumptions, namely (i) the nature of the stomatal response to elevated CO2 (coupling between photosynthesis and stomata was supported by the data); (ii) the roles of the leaf and atmospheric boundary layer (models which assumed multiple conductance terms in series predicted more decoupled fluxes than observed at the broadleaf site); (iii) the treatment of canopy interception (large intermodel variability, 2–15%); and (iv) the impact of soil moisture stress (process uncertainty in how models limit carbon and water fluxes during moisture stress). Overall, model predictions of the CO2 effect on WUE were reasonable (intermodel μ = approximately 28% ± 10%) compared to the observations (μ = approximately 30% ± 13%) at the well‐coupled coniferous site (Duke), but poor (intermodel μ = approximately 24% ± 6%; observations μ = approximately 38% ± 7%) at the broadleaf site (Oak Ridge). The study yields a framework for analysing and interpreting model predictions of transpiration responses to eCO2, and highlights key improvements to these types of models.
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BFBNIB, FZAB, GIS, IJS, KILJ, NLZOH, NUK, OILJ, SAZU, SBCE, SBMB, UL, UM, UPUK
Natural ecosystems store large amounts of carbon globally, as organisms absorb carbon from the atmosphere to build large, long-lasting, or slow-decaying structures such as tree bark or root systems. ...An ecosystem's carbon sequestration potential is tightly linked to its biological diversity. Yet when considering future projections, many carbon sequestration models fail to account for the role biodiversity plays in carbon storage. Here, we assess the consequences of plant biodiversity loss for carbon storage under multiple climate and land-use change scenarios. We link a macroecological model projecting changes in vascular plant richness under different scenarios with empirical data on relationships between biodiversity and biomass. We find that biodiversity declines from climate and land use change could lead to a global loss of between 7.44-103.14 PgC (global sustainability scenario) and 10.87-145.95 PgC (fossil-fueled development scenario). This indicates a self-reinforcing feedback loop, where higher levels of climate change lead to greater biodiversity loss, which in turn leads to greater carbon emissions and ultimately more climate change. Conversely, biodiversity conservation and restoration can help achieve climate change mitigation goals.
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