ORCID Profile
0000-0003-2482-1818
Current Organisation
Tsinghua University
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Terrestrial Ecology | Ecology |
Ecosystem Adaptation to Climate Change | Expanding Knowledge in the Biological Sciences | Flora, Fauna and Biodiversity at Regional or Larger Scales
Publisher: Elsevier BV
Date: 12-2018
Publisher: Springer Science and Business Media LLC
Date: 28-11-2016
DOI: 10.1038/SREP38020
Abstract: Both anthropogenic activities and climate change can affect the biogeochemical processes of natural wetland methanogenesis. Quantifying possible impacts of changing climate and wetland area on wetland methane (CH 4 ) emissions in China is important for improving our knowledge on CH 4 budgets locally and globally. However, their respective and combined effects are uncertain. We incorporated changes in wetland area derived from remote sensing into a dynamic CH 4 model to quantify the human and climate change induced contributions to natural wetland CH 4 emissions in China over the past three decades. Here we found that human-induced wetland loss contributed 34.3% to the CH 4 emissions reduction (0.92 TgCH 4 ), and climate change contributed 20.4% to the CH 4 emissions increase (0.31 TgCH 4 ), suggesting that decreasing CH 4 emissions due to human-induced wetland reductions has offset the increasing climate-driven CH 4 emissions. With climate change only, temperature was a dominant controlling factor for wetland CH 4 emissions in the northeast (high latitude) and Qinghai-Tibet Plateau (high altitude) regions, whereas precipitation had a considerable influence in relative arid north China. The inevitable uncertainties caused by the asynchronous for different regions or periods due to inter-annual or seasonal variations among remote sensing images should be considered in the wetland CH 4 emissions estimation.
Publisher: Springer Science and Business Media LLC
Date: 02-09-2019
Publisher: Wiley
Date: 28-09-2012
DOI: 10.1002/ACR.21727
Abstract: While strong evidence supports the role of physiotherapy in the co-management of patients with rheumatoid arthritis (RA), it remains unclear what constitutes the essential disease-specific knowledge and clinical skills required by community-based physiotherapists to effectively and safely deliver recommended care. This study aimed to identify essential disease-specific knowledge and skills, link these with evidence from clinical guidelines, and broadly determine the professional development (PD) needs and confidence related to the management of RA among physiotherapists. An international Delphi panel of rheumatologists, physiotherapists, and consumers (n = 27) identified essential disease-specific knowledge and clinical skills over 3 rounds. Physiotherapy-relevant recommendations from high-quality, contemporary clinical guidelines were linked to Delphi responses. Finally, an e-survey of PD needs among registered physiotherapists (n = 285) was undertaken. Overarching themes identified by the Delphi panel across the RA disease stages included the need for excellent communication, the importance of a multidisciplinary team and early referral, adoption of chronic disease management principles, and disease monitoring. Of the essential Delphi themes, 86.7% aligned with clinical guideline recommendations. Up to 77.5% of physiotherapists reported not being confident in managing patients with RA. Across the range of essential knowledge and skills themes, 45.1-93.5% and 71.1-95.2% of respondents, respectively, indicated they would benefit from or definitely need PD. To effectively manage RA, community-based physiotherapists require excellent communication skills and disease-specific knowledge, including understanding the role of the multidisciplinary team and the principles of early referral, chronic disease management, and monitoring. Physiotherapists identified a need for PD to develop these skills.
Publisher: Wiley
Date: 23-12-2010
DOI: 10.1111/J.1469-8137.2010.03579.X
Abstract: See also the Commentary by Midgley
Publisher: Copernicus GmbH
Date: 25-02-2014
Abstract: Abstract. Persistent ergences among the predictions of complex carbon cycle models include differences in the sign as well as the magnitude of the response of global terrestrial primary production to climate change. This and other problems with current models indicate an urgent need to re-assess the principles underlying the environmental controls of primary production. The global patterns of annual and maximum monthly terrestrial gross primary production (GPP) by C3 plants are explored here using a simple first-principles model based on the light-use efficiency formalism and the Farquhar model for C3 photosynthesis. The model is driven by incident photosynthetically active radiation (PAR) and remotely sensed green vegetation cover, with additional constraints imposed by low-temperature inhibition and CO2 limitation. The ratio of leaf-internal to ambient CO2 concentration in the model responds to growing-season mean temperature, atmospheric dryness (indexed by the cumulative water deficit, ΔE) and elevation, based on optimality theory. The greatest annual GPP is predicted for tropical moist forests, but the maximum (summer) monthly GPP can be as high or higher in boreal or temperate forests. These findings are supported by a new analysis of CO2 flux measurements. The explanation is simply based on the seasonal and latitudinal distribution of PAR combined with the physiology of photosynthesis. By successively imposing biophysical constraints, it is shown that partial vegetation cover – driven primarily by water shortage – represents the largest constraint on global GPP.
Publisher: Copernicus GmbH
Date: 13-05-2015
Abstract: Abstract. Dynamic global vegetation models (DGVMs) typically rely on plant functional types (PFTs), which are assigned distinct environmental tolerances and replace one another progressively along environmental gradients. Fixed values of traits are assigned to each PFT modelled trait variation along gradients is thus driven by PFT replacement. But empirical studies have revealed "universal" scaling relationships (quantitative trait variations with climate that are similar within and between species, PFTs and communities) and continuous, adaptive trait variation has been proposed to replace PFTs as the basis for next-generation DGVMs. Here we analyse quantitative leaf-trait variation on long temperature and moisture gradients in China with a view to understanding the relative importance of PFT replacement vs. continuous adaptive variation within PFTs. Leaf area (LA), specific leaf area (SLA), leaf dry matter content (LDMC) and nitrogen content of dry matter were measured on all species at 80 sites ranging from temperate to tropical climates and from dense forests to deserts. Chlorophyll fluorescence traits and carbon, phosphorus and potassium contents were measured at 47 sites. Generalized linear models were used to relate log-transformed trait values to growing-season temperature and moisture indices, with or without PFT identity as a predictor, and to test for differences in trait responses among PFTs. Continuous trait variation was found to be ubiquitous. Responses to moisture availability were generally similar within and between PFTs, but biophysical traits (LA, SLA and LDMC) of forbs and grasses responded differently from woody plants. SLA and LDMC responses to temperature were dominated by the prevalence of evergreen PFTs with thick, dense leaves at the warm end of the gradient. Nutrient (N, P and K) responses to climate gradients were generally similar within all PFTs. Area-based nutrients generally declined with moisture Narea and Karea declined with temperature, but Parea increased with temperature. Although the adaptive nature of many of these trait–climate relationships is understood qualitatively, a key challenge for modelling is to predict them quantitatively. Models must also take into account that community-level responses to climatic gradients can be influenced by shifts in PFT composition, such as the replacement of deciduous by evergreen trees, which may run either parallel or counter to trait variation within PFTs. The importance of PFT shifts varies among traits, being important for biophysical traits but less so for physiological and chemical traits.
Publisher: Springer Science and Business Media LLC
Date: 02-03-2015
DOI: 10.1038/NCLIMATE2550
Publisher: Elsevier BV
Date: 10-2007
DOI: 10.1016/J.SEMARTHRIT.2007.02.001
Abstract: To assess the safety and efficacy of combination therapy in recent-onset rheumatoid arthritis (RA), with dose adjustments determined by response, in a clinic setting over 3 years. Disease-modifying antirheumatic drug (DMARD)-naive patients with RA of median duration of 12 weeks (n = 61) attending an early arthritis clinic were treated with methotrexate, sulfasalazine, hydroxychloroquine, and fish oil. Dosage adjustments and additions of further DMARDs were contingent on response to therapy and tolerance. Outcome measures for efficacy were Disease Activity Score (DAS28), clinical remission, and modified Sharp radiographic score and for safety, adverse events, and DMARD withdrawal. At baseline, subjects had at least moderately active disease (mean +/- SD DAS28 was 5.3 +/- 1.1), impaired function as measured by the modified Health Assessment Questionnaire (mHAQ) (0.9 +/- 0.5), and 37% had bone erosions. By 3 months, 29% were in remission this increased to 54% at 3 years. The greatest fall in DAS28 and improvement in mHAQ scores occurred in the first 12 months. Erosions were detected in 62% at 3 years. The mean dose of parenteral glucocorticoid was equivalent to 0.1 mg/d of prednisolone. After 3 years, 48% remained on triple therapy fish oil was consumed by 75% of patients, and 21% used nonsteroidal anti-inflammatory drugs. Gastrointestinal intolerance was the most frequent unwanted event (leading to DMARD withdrawal in 17 patients). Sulfasalazine was most frequently withdrawn (30%). This implementation study demonstrates the feasibility, safety, and efficacy of combination therapy with inexpensive DMARDs, fish oil, and minimal glucocorticoid use, in routine clinical practice using predefined rules for dosage adjustment.
Publisher: Wiley
Date: 17-10-2022
DOI: 10.1111/GCB.16459
Abstract: Recent increases in vegetation greenness over much of the world reflect increasing CO 2 globally and warming in cold areas. However, the strength of the response to both CO 2 and warming in those areas appears to be declining for unclear reasons, contributing to large uncertainties in predicting how vegetation will respond to future global changes. Here, we investigated the changes of satellite‐observed peak season absorbed photosynthetically active radiation ( F max ) on the Tibetan Plateau between 1982 and 2016. Although climate trends are similar across the Plateau, we identified robust ergent responses (a greening of 0.31 ± 0.14% year −1 in drier regions and a browning of 0.12 ± 0.08% year −1 in wetter regions). Using an eco‐evolutionary optimality (EEO) concept of plant acclimation/adaptation, we propose a parsimonious modelling framework that quantitatively explains these changes in terms of water and energy limitations. Our model captured the variations in F max with a correlation coefficient ( r ) of .76 and a root mean squared error of .12 and predicted the ergent trends of greening (0.32 ± 0.19% year −1 ) and browning (0.07 ± 0.06% year −1 ). We also predicted the observed reduced sensitivities of F max to precipitation and temperature. The model allows us to explain these changes: Enhanced growing season cumulative radiation has opposite effects on water use and energy uptake. Increased precipitation has an overwhelmingly positive effect in drier regions, whereas warming reduces F max in wetter regions by increasing the cost of building and maintaining leaf area. Rising CO 2 stimulates vegetation growth by enhancing water‐use efficiency, but its effect on photosynthesis saturates. The large decrease in the sensitivity of vegetation to climate reflects a shift from water to energy limitation. Our study demonstrates the potential of EEO approaches to reveal the mechanisms underlying recent trends in vegetation greenness and provides further insight into the response of alpine ecosystems to ongoing climate change.
Publisher: Wiley
Date: 18-11-2016
DOI: 10.1111/NPH.14332
Publisher: SAGE Publications
Date: 07-05-2015
Abstract: Dynamic global vegetation models (DGVMs) typically track the material and energy cycles in ecosystems with finite plant functional types (PFTs). Increasingly, the community ecology and modelling studies recognize that current PFT scheme is not sufficient for simulating ecological processes. Recent advances in the study of plant functional traits (FTs) in community ecology provide a novel and feasible approach for the improvement of PFT-based DGVMs. This paper reviews the development of current DGVMs over recent decades. After characterizing the advantages and disadvantages of the PFT-based scheme, it summarizes trait-based theories and discusses the possibility of incorporating FTs into DGVMs. More importantly, this paper summarizes three strategies for constructing next-generation DGVMs with FTs. Finally, the method’s limitations, current challenges and future research directions for FT theory are discussed for FT theory. We strongly recommend the inclusion of several FTs, namely specific leaf area (SLA), leaf nitrogen content (LNC), carbon isotope composition of leaves (Leaf δ 13 C), the ratio between leaf-internal and ambient mole fractions of CO 2 (Leaf C i /C a ), seed mass and plant height. These are identified as the most important in constructing DGVMs based on FTs, which are also recognized as important ecological strategies for plants. The integration of FTs into dynamic vegetation models is a critical step towards improving the results of DGVM simulations communication and cooperation among ecologists and modellers is equally important for the development of the next generation of DGVMs.
Publisher: IOP Publishing
Date: 10-2021
Publisher: Science China Press., Co. Ltd.
Date: 19-07-2018
Publisher: Springer Science and Business Media LLC
Date: 28-09-2016
Publisher: Wiley
Date: 08-2022
Abstract: Leaf dry mass per unit area (LMA), carboxylation capacity ( V cmax ) and leaf nitrogen per unit area (N area ) and mass (N mass ) are key traits for plant functional ecology and ecosystem modelling. There is however no consensus about how these traits are regulated, or how they should be modelled. Here we confirm that observed leaf nitrogen across species and sites can be estimated well from observed LMA and V cmax at 25°C ( V cmax25 ). We then test the hypothesis that global variations of both quantities depend on climate variables in specific ways that are predicted by leaf‐level optimality theory, thus allowing both N area to be predicted as functions of the growth environment. A new global compilation of field measurements was used to quantify the empirical relationships of leaf N to V cmax25 and LMA. Relationships of observed V cmax25 and LMA to climate variables were estimated, and compared to independent theoretical predictions of these relationships. Soil effects were assessed by analysing biases in the theoretical predictions. LMA was the most important predictor of N area (increasing) and N mass (decreasing). About 60% of global variation across species and sites in observed N area , and 31% in N mass , could be explained by observed LMA and V cmax25 . These traits, in turn, were quantitatively related to climate variables, with significant partial relationships similar or indistinguishable from those predicted by optimality theory. Predicted trait values explained 21% of global variation in observed site‐mean V cmax25 , 43% in LMA and 31% in N area . Predicted V cmax25 was biased low on clay‐rich soils but predicted LMA was biased high, with compensating effects on N area . N area was overpredicted on organic soils. Synthesis . Global patterns of variation in observed site‐mean N area can be explained by climate‐induced variations in optimal V cmax25 and LMA. Leaf nitrogen should accordingly be modelled as a consequence (not a cause) of V cmax25 and LMA, both being optimized to the environment. Nitrogen limitation of plant growth would then be modelled principally via whole‐plant carbon allocation, rather than via leaf‐level traits. Further research is required to better understand and model the terrestrial nitrogen and carbon cycles and their coupling.
Publisher: Springer Science and Business Media LLC
Date: 15-12-2022
DOI: 10.1038/S41597-022-01884-4
Abstract: Plant functional traits represent adaptive strategies to the environment, linked to biophysical and biogeochemical processes and ecosystem functioning. Compilations of trait data facilitate research in multiple fields from plant ecology through to land-surface modelling. Here we present version 2 of the China Plant Trait Database, which contains information on morphometric, physical, chemical, photosynthetic and hydraulic traits from 1529 unique species in 140 sites spanning a ersity of vegetation types. Version 2 has five improvements compared to the previous version: (1) new data from a 4-km elevation transect on the edge of Tibetan Plateau, including alpine vegetation types not s led previously (2) inclusion of traits related to hydraulic processes, including specific sapwood conductance, the area ratio of sapwood to leaf, wood density and turgor loss point (3) inclusion of information on soil properties to complement the existing data on climate and vegetation (4) assessments and flagging the reliability of in idual trait measurements and (5) inclusion of standardized templates for systematical field s ling and measurements.
Publisher: Wiley
Date: 11-09-2018
DOI: 10.1111/NPH.15422
Abstract: Plant functional ecology requires the quantification of trait variation and its controls. Field measurements on 483 species at 48 sites across China were used to analyse variation in leaf traits, and assess their predictability. Principal components analysis (PCA) was used to characterize trait variation, redundancy analysis (RDA) to reveal climate effects, and RDA with variance partitioning to estimate separate and overlapping effects of site, climate, life-form and family membership. Four orthogonal dimensions of total trait variation were identified: leaf area (LA), internal-to-ambient CO
Publisher: Copernicus GmbH
Date: 22-11-2012
Abstract: Abstract. An extensive data set on net primary production (NPP) in China's forests is analysed with the help of two simple theoretically derived models based on the light use efficiency (LUE) and water use efficiency (WUE) concepts, respectively. The two models describe the data equally well, but their implied responses to [CO2] and temperature differ substantially. These responses are illustrated by sensitivity tests in which [CO2] is kept constant or doubled, temperatures are kept constant or increased by 3.5 K, and precipitation is changed by ±10%. Precipitation changes elicit similar responses in both models. But NPP in South China, especially, is reduced by warming in the LUE model, whereas it is increased in the WUE model. The [CO2] response of the WUE model is much larger than that of the LUE model. It is argued that the two models provide upper and lower bounds for this response, with the LUE model more realistic for forests. The differences between the two models illustrate some potential causes of the large differences (even in sign) in the global NPP response of different global vegetation models to temperature and [CO2].
Publisher: Copernicus GmbH
Date: 05-08-2019
DOI: 10.5194/GMD-2019-200
Abstract: Abstract. Terrestrial photosynthesis is the basis for vegetation growth and drives the land carbon cycle. Accurately simulating gross primary production (GPP, ecosystem-level apparent photosynthesis) is key for satellite monitoring and Earth System Model predictions under climate change. While robust models exist for describing leaf-level photosynthesis, predictions erge due to uncertain photosynthetic traits and parameters which vary on multiple spatial and temporal scales. Here, we describe and evaluate a gross primary production (GPP, photosynthesis per unit ground area) model, the P-model, that combines the Farquhar-von Caemmerer-Berry model for C3 photosynthesis with an optimality principle for the carbon assimilation-transpiration trade-off, and predicts a multi-day average light use efficiency (LUE) for any climate and C3 vegetation type. The model is forced here with satellite data for the fraction of absorbed photosynthetically active radiation and site-specific meteorological data and is evaluated against GPP estimates from a globally distributed network of ecosystem flux measurements. Although the P-model requires relatively few inputs and prescribed parameters, the R2 for predicted versus observed GPP based on the full model setup is 0.75 (8-day mean, 131 sites) – better than some state-of-the-art satellite data-driven light use efficiency models. The R2 is reduced to 0.69 when not accounting for the reduction in quantum yield at low temperatures and effects of low soil moisture on LUE. The R2 for the P-model-predicted LUE is 0.37 (means by site) and 0.53 (means by vegetation type). The P-model provides a simple but powerful method for predicting – rather than prescribing – light use efficiency and simulating terrestrial photosythesis across a wide range of conditions. The model is available as an R package (rpmodel).
Publisher: Copernicus GmbH
Date: 23-03-2020
DOI: 10.5194/EGUSPHERE-EGU2020-12097
Abstract: & & The distribution of leaf nitrogen (N& sub& L& /sub& ) within canopies has been discussed for decades in relation to the optimality hypothesis that predicts coordination of carboxylation capacity with absorbed light. Although an optimal (that is, proportional) response of both carboxylation capacity and N& sub& L& /sub& to light is extensively supported by field observations of variation among sites, the observed saturation curve of N& sub& L& /sub& within canopies seems to challenge the generality of that response. By considering dynamic light regimes, we propose an optimality-based theory that successfully reconciles the apparent conflict of observed N& sub& L& /sub& distribution within and between canopies. This theory proposes that due to the highly uneven temporal distribution of sun flecks, the light level to which understory leaves acclimate is much higher than the average light level. This proposition leads to a saturation curve for the vertical distribution of N& sub& L& /sub& . Our within-canopy data analysis supports this theory. Understorey leaves require significantly less N& sub& L& /sub& to achieve photosynthetic capacity as an acclimation to sun flecks. The contribution of structural and photosynthetic components to N& sub& L& /sub& redicted by the theory is quantitatively and consistently supported by global datasets of variation both within and between canopies.& &
Publisher: Copernicus GmbH
Date: 17-09-2015
Abstract: Abstract. Dynamic global vegetation models (DGVMs) typically rely on plant functional types (PFTs), which are assigned distinct environmental tolerances and replace one another progressively along environmental gradients. Fixed values of traits are assigned to each PFT modelled trait variation along gradients is thus driven by PFT replacement. But empirical studies have revealed "universal" scaling relationships (quantitative trait variations with climate that are similar within and between species, PFTs and communities) and continuous, adaptive trait variation has been proposed to replace PFTs as the basis for next-generation DGVMs. Here we analyse quantitative leaf-trait variation on long temperature and moisture gradients in China with a view to understanding the relative importance of PFT replacement vs. continuous adaptive variation within PFTs. Leaf area (LA), specific leaf area (SLA), leaf dry matter content (LDMC) and nitrogen content of dry matter were measured on all species at 80 sites ranging from temperate to tropical climates and from dense forests to deserts. Chlorophyll fluorescence traits and carbon, phosphorus and potassium contents were measured at 47 sites. Generalized linear models were used to relate log-transformed trait values to growing-season temperature and moisture indices, with or without PFT identity as a predictor, and to test for differences in trait responses among PFTs. Continuous trait variation was found to be ubiquitous. Responses to moisture availability were generally similar within and between PFTs, but biophysical traits (LA, SLA and LDMC) of forbs and grasses responded differently from woody plants. SLA and LDMC responses to temperature were dominated by the prevalence of evergreen PFTs with thick, dense leaves at the warm end of the gradient. Nutrient (N, P and K) responses to climate gradients were generally similar within all PFTs. Area-based nutrients generally declined with moisture Narea and Karea declined with temperature, but Parea increased with temperature. Although the adaptive nature of many of these trait-climate relationships is understood qualitatively, a key challenge for modelling is to predict them quantitatively. Models must take into account that community-level responses to climatic gradients can be influenced by shifts in PFT composition, such as the replacement of deciduous by evergreen trees, which may run either parallel or counter to trait variation within PFTs. The importance of PFT shifts varies among traits, being important for biophysical traits but less so for physiological and chemical traits. Finally, models should take account of the ersity of trait values that is found in all sites and PFTs, representing the "pool" of variation that is locally available for the natural adaptation of ecosystem function to environmental change.
Publisher: Wiley
Date: 24-02-2020
DOI: 10.1111/GCB.14980
Publisher: Springer Science and Business Media LLC
Date: 11-05-2020
Publisher: Wiley
Date: 12-09-2020
DOI: 10.1111/GCB.15276
Abstract: The maximum rate of carboxylation ( V cmax ) is an essential leaf trait determining the photosynthetic capacity of plants. Existing approaches for estimating V cmax at large scale mainly rely on empirical relationships with proxies such as leaf nitrogen/chlorophyll content or hyperspectral reflectance, or on complicated inverse models from gross primary production or solar‐induced fluorescence. A novel mechanistic approach based on the assumption that plants optimize resource investment coordinating with environment and growth has been shown to accurately predict C3 plant V cmax based on mean growing season environmental conditions. However, the ability of optimality theory to explain seasonal variation in V cmax has not been fully investigated. Here, we adapt an optimality‐based model to simulate daily V cmax,25C ( V cmax at a standardized temperature of 25°C) by incorporating the effects of antecedent environment, which affects current plant functioning, and dynamic light absorption, which coordinates with plant functioning. We then use seasonal V cmax,25C field measurements from 10 sites across erse ecosystems to evaluate model performance. Overall, the model explains about 83% of the seasonal variation in C3 plant V cmax,25C across the 10 sites, with a medium root mean square error of 12.3 μmol m −2 s −1 , which suggests that seasonal changes in V cmax,25C are consistent with optimal plant function. We show that failing to account for acclimation to antecedent environment or coordination with dynamic light absorption dramatically decreases estimation accuracy. Our results show that optimality‐based approach can accurately reproduce seasonal variation in canopy photosynthetic potential, and suggest that incorporating such theory into next‐generation trait‐based terrestrial biosphere models would improve predictions of global photosynthesis.
Publisher: Cold Spring Harbor Laboratory
Date: 02-03-2021
DOI: 10.1101/2021.03.02.433324
Abstract: The coupling between water loss and carbon dioxide uptake drives the coordination of plant hydraulic and photosynthetic traits. Analysing multi-species measurements on a 3000 m elevation gradient, we found that hydraulic and leaf-economic traits were less plastic, and more closely associated with phylogeny, than photosynthetic traits. The two trait sets are linked by the sapwood-to-leaf area ratio (Huber value, v H ), shown here to be codetermined by sapwood hydraulic conductance ( K S ), leaf mass-per-area (LMA) and photosynthetic capacity ( V cmax ). Substantial hydraulic ersity was related to the trade-off between K S and v H . Leaf drought tolerance (inferred from turgor loss point, –π tlp ) increased with wood density, but the trade-off between hydraulic efficiency ( K S ) and –π tlp was weak. The least-cost optimality framework was extended to predict trait ( K S -dominated) and environmental (temperature-dominated) effects on v H . These results suggest an approach to include photosynthetic-hydraulic coordination in land-surface models however, prediction of non-plastic trait distributions remains a challenge.
Publisher: Wiley
Date: 04-01-2019
DOI: 10.1111/ELE.13210
Publisher: Copernicus GmbH
Date: 15-04-2016
DOI: 10.5194/GMD-2016-49
Abstract: Abstract. Bioclimatic indices for use in studies of ecosystem function, species distribution, and vegetation dynamics under changing climate scenarios depend on estimates of surface fluxes and other quantities, such as radiation, evapotranspiration and soil moisture, for which direct observations are sparse. These quantities can be derived indirectly from meteorological variables, such as air temperature, precipitation and cloudiness. Here we present a consolidated set of Simple Process-Led Algorithms for Simulating Habitats (SPLASH) allowing robust approximations of key quantities at ecologically relevant time scales. We specify equations, derivations, simplifications and assumptions for the estimation of daily and monthly quantities of top-of-the-atmosphere solar radiation, net surface radiation, photosynthetic photon flux density, evapotranspiration (potential, equilibrium and actual), condensation, soil moisture, and runoff, based on analysis of their relationship to fundamental climatic drivers. SPLASH, as presented here, is designed for application at discrete locations however, the same methodology can naturally be applied to spatial grids. The climatic drivers include a minimum of three meteorological inputs: precipitation, air temperature, and either fraction of bright sunshine hours or fractional cloud cover. Indices, such as the moisture index, the climatic water deficit, and the Priestley-Taylor coefficient, are also defined. The SPLASH code is transcribed in C++, FORTRAN, Python, and R. One year of results from a specific location are provided to exemplify the daily and monthly model outputs, following a two-year spin-up of soil moisture content.
Publisher: Elsevier BV
Date: 06-2017
DOI: 10.1016/J.TREE.2017.02.020
Abstract: Forest canopies are dynamic interfaces between organisms and atmosphere, providing buffered microclimates and complex microhabitats. Canopies form vertically stratified ecosystems interconnected with other strata. Some forest bio ersity patterns and food webs have been documented and measurements of ecophysiology and biogeochemical cycling have allowed analyses of large-scale transfer of CO
Publisher: Copernicus GmbH
Date: 31-10-2014
Abstract: Abstract. Persistent ergences among the predictions of complex carbon-cycle models include differences in the sign as well as the magnitude of the response of global terrestrial primary production to climate change. Such problems with current models indicate an urgent need to reassess the principles underlying the environmental controls of primary production. The global patterns of annual and maximum monthly terrestrial gross primary production (GPP) by C3 plants are explored here using a simple first-principles model based on the light-use efficiency formalism and the Farquhar model for C3 photosynthesis. The model is driven by incident photosynthetically active radiation (PAR) and remotely sensed green-vegetation cover, with additional constraints imposed by low-temperature inhibition and CO2 limitation. The ratio of leaf-internal to ambient CO2 concentration in the model responds to growing-season mean temperature, atmospheric dryness (indexed by the cumulative water deficit, Δ E) and elevation, based on an optimality theory. The greatest annual GPP is predicted for tropical moist forests, but the maximum (summer) monthly GPP can be as high, or higher, in boreal or temperate forests. These findings are supported by a new analysis of CO2 flux measurements. The explanation is simply based on the seasonal and latitudinal distribution of PAR combined with the physiology of photosynthesis. By successively imposing biophysical constraints, it is shown that partial vegetation cover – driven primarily by water shortage – represents the largest constraint on global GPP.
Publisher: Springer Science and Business Media LLC
Date: 04-09-2017
DOI: 10.1038/S41477-017-0006-8
Abstract: Gross primary production (GPP)-the uptake of carbon dioxide (CO
Publisher: Wiley
Date: 31-12-2019
DOI: 10.1111/GCB.14904
Abstract: Plant traits—the morphological, anatomical, physiological, biochemical and phenological characteristics of plants—determine how plants respond to environmental factors, affect other trophic levels, and influence ecosystem properties and their benefits and detriments to people. Plant trait data thus represent the basis for a vast area of research spanning from evolutionary biology, community and functional ecology, to bio ersity conservation, ecosystem and landscape management, restoration, biogeography and earth system modelling. Since its foundation in 2007, the TRY database of plant traits has grown continuously. It now provides unprecedented data coverage under an open access data policy and is the main plant trait database used by the research community worldwide. Increasingly, the TRY database also supports new frontiers of trait‐based plant research, including the identification of data gaps and the subsequent mobilization or measurement of new data. To support this development, in this article we evaluate the extent of the trait data compiled in TRY and analyse emerging patterns of data coverage and representativeness. Best species coverage is achieved for categorical traits—almost complete coverage for ‘plant growth form’. However, most traits relevant for ecology and vegetation modelling are characterized by continuous intraspecific variation and trait–environmental relationships. These traits have to be measured on in idual plants in their respective environment. Despite unprecedented data coverage, we observe a humbling lack of completeness and representativeness of these continuous traits in many aspects. We, therefore, conclude that reducing data gaps and biases in the TRY database remains a key challenge and requires a coordinated approach to data mobilization and trait measurements. This can only be achieved in collaboration with other initiatives.
Publisher: Wiley
Date: 16-07-2021
DOI: 10.1111/NPH.17579
Abstract: Leaf trait relationships are widely used to predict ecosystem function in terrestrial biosphere models (TBMs), in which leaf maximum carboxylation capacity ( V c,max ), an important trait for modelling photosynthesis, can be inferred from other easier‐to‐measure traits. However, whether trait– V c,max relationships are robust across different forest types remains unclear. Here we used measurements of leaf traits, including one morphological trait (leaf mass per area), three biochemical traits (leaf water content, area‐based leaf nitrogen content, and leaf chlorophyll content), one physiological trait ( V c,max ), as well as leaf reflectance spectra, and explored their relationships within and across three contrasting forest types in China. We found weak and forest type‐specific relationships between V c,max and the four morphological and biochemical traits ( R 2 ≤ 0.15), indicated by significantly changing slopes and intercepts across forest types. By contrast, reflectance spectroscopy effectively collapsed the differences in the trait– V c,max relationships across three forest biomes into a single robust model for V c,max ( R 2 = 0.77), and also accurately estimated the four traits ( R 2 = 0.75–0.94). These findings challenge the traditional use of the empirical trait– V c,max relationships in TBMs for estimating terrestrial plant photosynthesis, but also highlight spectroscopy as an efficient alternative for characterising V c,max and multitrait variability, with critical insights into ecosystem modelling and functional trait ecology.
Publisher: Wiley
Date: 24-08-2021
DOI: 10.1111/NPH.17656
Abstract: Close coupling between water loss and carbon dioxide uptake requires coordination of plant hydraulics and photosynthesis. However, there is still limited information on the quantitative relationships between hydraulic and photosynthetic traits. We propose a basis for these relationships based on optimality theory, and test its predictions by analysis of measurements on 107 species from 11 sites, distributed along a nearly 3000‐m elevation gradient. Hydraulic and leaf economic traits were less plastic, and more closely associated with phylogeny, than photosynthetic traits. The two sets of traits were linked by the sapwood to leaf area ratio (Huber value, v H ). The observed coordination between v H and sapwood hydraulic conductivity ( K S ) and photosynthetic capacity ( V cmax ) conformed to the proposed quantitative theory. Substantial hydraulic ersity was related to the trade‐off between K S and v H . Leaf drought tolerance (inferred from turgor loss point, –Ψ tlp ) increased with wood density, but the trade‐off between hydraulic efficiency ( K S ) and –Ψ tlp was weak. Plant trait effects on v H were dominated by variation in K S , while effects of environment were dominated by variation in temperature. This research unifies hydraulics, photosynthesis and the leaf economics spectrum in a common theoretical framework, and suggests a route towards the integration of photosynthesis and hydraulics in land‐surface models.
Publisher: Copernicus GmbH
Date: 26-03-2020
Abstract: Abstract. Terrestrial photosynthesis is the basis for vegetation growth and drives the land carbon cycle. Accurately simulating gross primary production (GPP, ecosystem-level apparent photosynthesis) is key for satellite monitoring and Earth system model predictions under climate change. While robust models exist for describing leaf-level photosynthesis, predictions erge due to uncertain photosynthetic traits and parameters which vary on multiple spatial and temporal scales. Here, we describe and evaluate a GPP (photosynthesis per unit ground area) model, the P-model, that combines the Farquhar–von Caemmerer–Berry model for C3 photosynthesis with an optimality principle for the carbon assimilation–transpiration trade-off, and predicts a multi-day average light use efficiency (LUE) for any climate and C3 vegetation type. The model builds on the theory developed in Prentice et al. (2014) and Wang et al. (2017a) and is extended to include low temperature effects on the intrinsic quantum yield and an empirical soil moisture stress factor. The model is forced with site-level data of the fraction of absorbed photosynthetically active radiation (fAPAR) and meteorological data and is evaluated against GPP estimates from a globally distributed network of ecosystem flux measurements. Although the P-model requires relatively few inputs, the R2 for predicted versus observed GPP based on the full model setup is 0.75 (8 d mean, 126 sites) – similar to comparable satellite-data-driven GPP models but without predefined vegetation-type-specific parameters. The R2 is reduced to 0.70 when not accounting for the reduction in quantum yield at low temperatures and effects of low soil moisture on LUE. The R2 for the P-model-predicted LUE is 0.32 (means by site) and 0.48 (means by vegetation type). Applying this model for global-scale simulations yields a total global GPP of 106–122 Pg C yr−1 (mean of 2001–2011), depending on the fAPAR forcing data. The P-model provides a simple but powerful method for predicting – rather than prescribing – light use efficiency and simulating terrestrial photosynthesis across a wide range of conditions. The model is available as an R package (rpmodel).
Publisher: Wiley
Date: 30-03-2022
Abstract: Leaf morphological traits vary systematically along climatic gradients. However, recent studies in plant functional ecology have mainly analysed quantitative traits, while numerical models of species distributions and vegetation function have focused on traits associated with resource acquisition both ignore the wider functional significance of leaf morphology. A dataset comprising 22 leaf morphological traits for 662 woody species from 92 sites, representing all biomes present in China, was subjected to multivariate analysis in order to identify leading dimensions of trait covariation (correspondence analysis), quantify climatic and phylogenetic contributions (canonical correspondence analysis with variation partitioning) and characterise co‐occurring trait syndromes ( k ‐means clustering) and their climatic preferences. Three axes accounted for % of trait variation in both evergreen and deciduous species. Moisture index, precipitation seasonality and growing‐season temperature explained 8%–10% of trait variation family 15%–32%. Microphyll or larger, mid‐ to dark green leaves with drip tips in wetter climates contrasted with nanophyll or smaller glaucous leaves without drip tips in drier climates. Thick, entire leaves in less seasonal climates contrasted with thin, marginal dissected, aromatic and involute/revolute leaves in more seasonal climates. Thick, involute, hairy leaves in colder climates contrasted with thin leaves with marked surface structures (surface patterning) in warmer climates. Distinctive trait clusters were linked to the driest and most seasonal climates, for ex le the clustering of picophyll, fleshy and succulent leaves in the driest climates and leptophyll, linear, dissected, revolute or involute and aromatic leaves in regions with highly seasonal rainfall. Several trait clusters co‐occurred in wetter climates, including clusters characterised by microphyll, moderately thick, patent and entire leaves or notophyll, waxy, dark green leaves. Synthesis . The plastic response of size, shape, colour and other leaf morphological traits to climate is muted, thus their apparent shift along climate gradients reflects plant adaptations to environment at a community level as determined by species replacement. Information on leaf morphological traits, widely available in floras, could be used to strengthen predictive models of species distribution and vegetation function.
Publisher: Copernicus GmbH
Date: 03-01-2013
Abstract: Abstract. A process-oriented niche specification (PONS) model was constructed to quantify climatic controls on the distribution of ecosystems, based on the vegetation map of China. PONS uses general hypotheses about bioclimatic controls to provide a "bridge" between statistical niche models and more complex process-based models. Canonical correspondence analysis provided an overview of relationships between the abundances of 55 plant communities in 0.1° grid cells and associated mean values of 20 predictor variables. Of these, GDD (accumulated degree days above 0 °C) Cramer–Prentice α (an estimate of the ratio of actual to equilibrium evapotranspiration) and mGDD5 (mean temperature during the period above 5 °C) showed the greatest predictive power. These three variables were used to develop generalized linear models for the probability of occurrence of 16 vegetation classes, aggregated from the original 55 types by k-means clustering according to bioclimatic similarity. Each class was hypothesized to possess a unimodal relationship to each bioclimate variable, independently of the other variables. A simple calibration was used to generate vegetation maps from the predicted probabilities of the classes. Modelled and observed vegetation maps showed good to excellent agreement (κ = 0.745). A sensitivity study examined modelled responses of vegetation distribution to spatially uniform changes in temperature, precipitation and [CO2], the latter included via an offset to α (based on an independent, data-based light use efficiency model for forest net primary production). Warming shifted the boundaries of most vegetation classes northward and westward while temperate steppe and desert replaced alpine tundra and steppe in the southeast of the Tibetan Plateau. Increased precipitation expanded mesic vegetation at the expense of xeric vegetation. The effect of [CO2] doubling was roughly equivalent to increasing precipitation by ∼ 30%, favouring woody vegetation types, particularly in northern China. Agricultural zones in northern China responded most strongly to warming, but also benefited from increases in precipitation and [CO2]. These results broadly conform to previously published findings made with the process-based model BIOME4, but they add regional detail and realism and extend the earlier results to include cropping systems. They provide a potential basis for a broad-scale assessment of global change impacts on natural and managed ecosystems.
Publisher: Springer Science and Business Media LLC
Date: 17-05-2014
Publisher: Wiley
Date: 28-10-2023
DOI: 10.1111/NPH.19355
Publisher: American Association for the Advancement of Science (AAAS)
Date: 20-01-2023
Abstract: The life span of leaves increases with their mass per unit area (LMA). It is unclear why. Here, we show that this empirical generalization (the foundation of the worldwide leaf economics spectrum) is a consequence of natural selection, maximizing average net carbon gain over the leaf life cycle. Analyzing two large leaf trait datasets, we show that evergreen and deciduous species with erse construction costs (assumed proportional to LMA) are selected by light, temperature, and growing-season length in different, but predictable, ways. We quantitatively explain the observed ergent latitudinal trends in evergreen and deciduous LMA and show how local distributions of LMA arise by selection under different environmental conditions acting on the species pool. These results illustrate how optimality principles can underpin a new theory for plant geography and terrestrial carbon dynamics.
Publisher: Wiley
Date: 27-12-2017
DOI: 10.1002/ECY.2091
Abstract: Plant functional traits provide information about adaptations to climate and environmental conditions, and can be used to explore the existence of alternative plant strategies within ecosystems. Trait data are also increasingly being used to provide parameter estimates for vegetation models. Here we present a new database of plant functional traits from China. Most global climate and vegetation types can be found in China, and thus the database is relevant for global modeling. The China Plant Trait Database contains information on morphometric, physical, chemical, and photosynthetic traits from 122 sites spanning the range from boreal to tropical, and from deserts and steppes through woodlands and forests, including montane vegetation. Data collection at each site was based either on s ling the dominant species or on a stratified s ling of each ecosystem layer. The database contains information on 1,215 unique species, though many species have been s led at multiple sites. The original field identifications have been taxonomically standardized to the Flora of China. Similarly, derived photosynthetic traits, such as electron-transport and carboxylation capacities, were calculated using a standardized method. To facilitate trait-environment analyses, the database also contains detailed climate and vegetation information for each site. The data set is released under a Creative Commons BY license. When using the data set, we kindly request that you cite this article, recognizing the hard work that went into collecting the data and the authors' willingness to make it publicly available.
Publisher: Copernicus GmbH
Date: 13-05-2015
Publisher: Elsevier BV
Date: 02-2014
DOI: 10.1016/J.PLANTSCI.2013.10.007
Abstract: Oryza sativa and Oryza glaberrima have been selected to acquire and partition resources efficiently as part of the process of domestication. However, genetic ersity in cultivated rice is limited compared to wild Oryza species, in spite of 120,000 genotypes being held in gene banks. By contrast, there is untapped ersity in the more than 20 wild species of Oryza, some having been collected from just a few coastal locations (e.g. Oryza schlechteri), while others are widely distributed (e.g. Oryza nivara and Oryza rufipogon). The extent of DNA sequence ersity and phenotypic variation is still being established in wild Oryza, with genetic barriers suggesting a vast range of morphologies and function even within species, such as has been demonstrated for Oryza meridionalis. With increasing climate variability and attempts to make more marginal land arable, abiotic and biotic stresses will be managed over the coming decades by tapping into the genetic ersity of wild relatives of O. sativa. To help create a more targeted approach to sourcing wild rice germplasm for abiotic stress tolerance, we have created a climate distribution map by plotting the natural occurrence of all Oryza species against corresponding temperature and moisture data. We then discuss interspecific variation in phenotype and its significance for rice, followed by a discussion of ways to integrate germplasm from wild relatives into domesticated rice.
Publisher: Wiley
Date: 14-04-2023
DOI: 10.1111/GEB.13680
Abstract: Leaf traits are central to plant function, and key variables in ecosystem models. However recently published global trait maps, made by applying statistical or machine‐learning techniques to large compilations of trait and environmental data, differ substantially from one another. This paper aims to demonstrate the potential of an alternative approach, based on eco‐evolutionary optimality theory, to yield predictions of spatio‐temporal patterns in leaf traits that can be independently evaluated. Global patterns of community‐mean specific leaf area (SLA) and photosynthetic capacity ( V cmax ) are predicted from climate via existing optimality models. Then leaf nitrogen per unit area ( N area ) and mass ( N mass ) are inferred using their (previously derived) empirical relationships to SLA and V cmax . Trait data are thus reserved for testing model predictions across sites. Temporal trends can also be predicted, as consequences of environmental change, and compared to those inferred from leaf‐level measurements and/or remote‐sensing methods, which are an increasingly important source of information on spatio‐temporal variation in plant traits. Model predictions evaluated against site‐mean trait data from 2,000 sites in the Plant Trait database yielded R 2 = 73% for SLA, 38% for N mass and 28% for N area . Declining species‐level N mass , and increasing community‐level SLA, have both been recently reported and were both correctly predicted. Leaf‐trait mapping via optimality theory holds promise for macroecological applications, including an improved understanding of community leaf‐trait responses to environmental change.
Publisher: Springer Science and Business Media LLC
Date: 08-11-2016
DOI: 10.1038/NCOMMS13428
Abstract: Terrestrial ecosystems play a significant role in the global carbon cycle and offset a large fraction of anthropogenic CO 2 emissions. The terrestrial carbon sink is increasing, yet the mechanisms responsible for its enhancement, and implications for the growth rate of atmospheric CO 2 , remain unclear. Here using global carbon budget estimates, ground, atmospheric and satellite observations, and multiple global vegetation models, we report a recent pause in the growth rate of atmospheric CO 2 , and a decline in the fraction of anthropogenic emissions that remain in the atmosphere, despite increasing anthropogenic emissions. We attribute the observed decline to increases in the terrestrial sink during the past decade, associated with the effects of rising atmospheric CO 2 on vegetation and the slowdown in the rate of warming on global respiration. The pause in the atmospheric CO 2 growth rate provides further evidence of the roles of CO 2 fertilization and warming-induced respiration, and highlights the need to protect both existing carbon stocks and regions, where the sink is growing rapidly.
Publisher: American Geophysical Union (AGU)
Date: 03-2021
DOI: 10.1029/2020JG005951
Abstract: As a region that is highly sensitive to global climate change, the Tibetan Plateau (TP) experiences an intra‐seasonal soil water deficient due to the reduced precipitation during the South Asia monsoon (SAM) breaks. Few studies have investigated the impact of SAM breaks on TP ecological processes, although a number of studies have explored the effects of inter‐annual and decadal climate variability. In this study, the response of vegetation activity to SAM breaks was investigated. The data used are: (1) soil moisture from in situ, satellite remote sensing and data assimilation and (2) the normalized difference vegetation index (NDVI) and solar‐induced chlorophyll fluorescence (SIF). We found that in the SAM break‐impacted region, which is distributed in the central‐eastern part of TP, photosynthesis become more active during SAM breaks. And temporal variability in the photosynthesis of this region is controlled mainly by solar radiation variability and has little sensitivity to soil moisture. We adopted a diagnostic process‐based modeling approach to examine the causes of enhanced plant activity during SAM breaks on the central‐eastern TP. Our analysis indicates that more carbon assimilated by photosynthesis in the reduced precipitation is stimulated by increases in solar radiation absorbed and temperature. This study highlights the importance of sub‐seasonal climate variability for characterizing the relationship between vegetation and climate.
Publisher: No publisher found
Date: 2017
DOI: 10.1111/GCB.13726
Abstract: Methane (CH
Publisher: Copernicus GmbH
Date: 04-09-2013
Abstract: Abstract. A process-oriented niche specification (PONS) model was constructed to quantify climatic controls on the distribution of ecosystems, based on the vegetation map of China. PONS uses general hypotheses about bioclimatic controls to provide a "bridge" between statistical niche models and more complex process-based models. Canonical correspondence analysis provided an overview of relationships between the abundances of 55 plant communities in 0.1° grid cells and associated mean values of 20 predictor variables. Of these, GDD0 (accumulated degree days above 0 °C), Cramer–Prentice α (an estimate of the ratio of actual to equilibrium evapotranspiration) and mGDD5 (mean temperature during the period above 5 °C) showed the greatest predictive power. These three variables were used to develop generalized linear models for the probability of occurrence of 16 vegetation classes, aggregated from the original 55 types by k-means clustering according to bioclimatic similarity. Each class was hypothesized to possess a unimodal relationship to each bioclimate variable, independently of the other variables. A simple calibration was used to generate vegetation maps from the predicted probabilities of the classes. Modelled and observed vegetation maps showed good to excellent agreement (κ = 0.745). A sensitivity study examined modelled responses of vegetation distribution to spatially uniform changes in temperature, precipitation and [CO2], the latter included via an offset to α (based on an independent, data-based light use efficiency model for forest net primary production). Warming shifted the boundaries of most vegetation classes northward and westward while temperate steppe and desert replaced alpine tundra and steppe in the southeast of the Tibetan Plateau. Increased precipitation expanded mesic vegetation at the expense of xeric vegetation. The effect of [CO2] doubling was roughly equivalent to increasing precipitation by ~ 30%, favouring woody vegetation types, particularly in northern China. Agricultural zones in northern China responded most strongly to warming, but also benefited from increases in precipitation and [CO2]. These results broadly conform to previously published findings made with the process-based model BIOME4, but they add regional detail and realism and extend the earlier results to include cropping systems. They provide a potential basis for a broad-scale assessment of global change impacts on natural and managed ecosystems.
Publisher: Springer Science and Business Media LLC
Date: 07-04-2016
DOI: 10.1038/SREP24110
Abstract: Increasing evidence indicates that current dynamic global vegetation models (DGVMs) have suffered from insufficient realism and are difficult to improve, particularly because they are built on plant functional type (PFT) schemes. Therefore, new approaches, such as plant trait-based methods, are urgently needed to replace PFT schemes when predicting the distribution of vegetation and investigating vegetation sensitivity. As an important direction towards constructing next-generation DGVMs based on plant functional traits, we propose a novel approach for modelling vegetation distributions and analysing vegetation sensitivity through trait-climate relationships in China. The results demonstrated that a Gaussian mixture model (GMM) trained with a LMA-N mass -LAI data combination yielded an accuracy of 72.82% in simulating vegetation distribution, providing more detailed parameter information regarding community structures and ecosystem functions. The new approach also performed well in analyses of vegetation sensitivity to different climatic scenarios. Although the trait-climate relationship is not the only candidate useful for predicting vegetation distributions and analysing climatic sensitivity, it sheds new light on the development of next-generation trait-based DGVMs.
Publisher: Wiley
Date: 22-04-2022
DOI: 10.1111/NPH.18076
Abstract: Nitrogen (N) limitation has been considered as a constraint on terrestrial carbon uptake in response to rising CO 2 and climate change. By extension, it has been suggested that declining carboxylation capacity ( V cmax ) and leaf N content in enhanced‐CO 2 experiments and satellite records signify increasing N limitation of primary production. We predicted V cmax using the coordination hypothesis and estimated changes in leaf‐level photosynthetic N for 1982–2016 assuming proportionality with leaf‐level V cmax at 25°C. The whole‐canopy photosynthetic N was derived using satellite‐based leaf area index (LAI) data and an empirical extinction coefficient for V cmax , and converted to annual N demand using estimated leaf turnover times. The predicted spatial pattern of V cmax shares key features with an independent reconstruction from remotely sensed leaf chlorophyll content. Predicted leaf photosynthetic N declined by 0.27% yr −1 , while observed leaf (total) N declined by 0.2–0.25% yr −1 . Predicted global canopy N (and N demand) declined from 1996 onwards, despite increasing LAI. Leaf‐level responses to rising CO 2 , and to a lesser extent temperature, may have reduced the canopy requirement for N by more than rising LAI has increased it. This finding provides an alternative explanation for declining leaf N that does not depend on increasing N limitation.
Publisher: Copernicus GmbH
Date: 14-02-2017
Abstract: Abstract. Bioclimatic indices for use in studies of ecosystem function, species distribution, and vegetation dynamics under changing climate scenarios depend on estimates of surface fluxes and other quantities, such as radiation, evapotranspiration and soil moisture, for which direct observations are sparse. These quantities can be derived indirectly from meteorological variables, such as near-surface air temperature, precipitation and cloudiness. Here we present a consolidated set of simple process-led algorithms for simulating habitats (SPLASH) allowing robust approximations of key quantities at ecologically relevant timescales. We specify equations, derivations, simplifications, and assumptions for the estimation of daily and monthly quantities of top-of-the-atmosphere solar radiation, net surface radiation, photosynthetic photon flux density, evapotranspiration (potential, equilibrium, and actual), condensation, soil moisture, and runoff, based on analysis of their relationship to fundamental climatic drivers. The climatic drivers include a minimum of three meteorological inputs: precipitation, air temperature, and fraction of bright sunshine hours. Indices, such as the moisture index, the climatic water deficit, and the Priestley–Taylor coefficient, are also defined. The SPLASH code is transcribed in C++, FORTRAN, Python, and R. A total of 1 year of results are presented at the local and global scales to exemplify the spatiotemporal patterns of daily and monthly model outputs along with comparisons to other model results.
Publisher: Elsevier BV
Date: 11-2019
DOI: 10.1016/J.JENVMAN.2019.109403
Abstract: The world is experiencing serious soil losses. Soil erosion has become an important environmental problem in certain regions and is strongly affected by climate and land use changes. By selecting and reviewing 13 extensively used soil water erosion models (SWEMs) from the published literature, we summarize the current model-based knowledge on how climate factors (e.g., rainfall, freeze-thaw cycles, rainstorms, temperature and atmospheric CO
Publisher: Springer Science and Business Media LLC
Date: 20-01-2011
Publisher: Copernicus GmbH
Date: 03-03-2021
DOI: 10.5194/EGUSPHERE-EGU21-1026
Abstract: & & Evapotranspiration (ET) links the water and carbon cycles in the atmosphere, hydrosphere and biosphere, and is of great important in earth system science, hydrology and resource management researches. Commonly used ET estimating approaches usually contains type-based parameters, which requires calibration and associates with land cover product. Parameterization structure, representativity of training group and accuracy of land cover information all influences the performance of model extrapolation. In this study, we develop an ET modelling framework based on the hypothesis that canopy conductance acclimates to plant growth conditions so that the total costs of maintaining carboxylation and transpiration capacities are minimized. This is combined with the principle of co-ordination between the light- and Rubisco-limited rates of photosynthesis to predict gross primary production (GPP). Transpiration (T) is predicted from GPP via canopy conductance. No plant type- or biome-specific parameters are used. ET is estimated from T by calibrating a site-specific (but time-invariant) ratio of modelled average T to observed average ET. Predicted GPP were well supported by (weekly) GPP records at 112 widely distributed eddy-covariance flux sites (FLUXNET 2015 dataset), with R& sup& & /sup& = 0.61, and RMSE = 2.73gC/day (N = 30129). ET were also well supported at site scale, with R& sup& & /sup& = 0.65, and RMSE = 0.73mm/day (N = 30129). Global ET mapping was carried out with the help of Google Earth Engine (GEE). Basin-scale water balance validation in several globally distributed watersheds also indicates the robustness of our model.& &
Publisher: American Association for the Advancement of Science (AAAS)
Date: 09-2017
Abstract: Leaf size varies by over a 100,000-fold among species worldwide. Although 19th-century plant geographers noted that the wet tropics harbor plants with exceptionally large leaves, the latitudinal gradient of leaf size has not been well quantified nor the key climatic drivers convincingly identified. Here, we characterize worldwide patterns in leaf size. Large-leaved species predominate in wet, hot, sunny environments small-leaved species typify hot, sunny environments only in arid conditions small leaves are also found in high latitudes and elevations. By modeling the balance of leaf energy inputs and outputs, we show that daytime and nighttime leaf-to-air temperature differences are key to geographic gradients in leaf size. This knowledge can enrich "next-generation" vegetation models in which leaf temperature and water use during photosynthesis play key roles.
Publisher: Cold Spring Harbor Laboratory
Date: 08-02-2021
DOI: 10.1101/2021.02.07.430028
Abstract: The worldwide leaf economics spectrum relates leaf lifespan (LL) to leaf dry mass per unit area (LMA) 1 . By combining three well-supported principles 2-4 , we show that an isometric relationship between these two quantities maximizes the leaf’s net carbon gain. This theory predicts a spectrum of equally competent LMA-LL combinations in any given environment, and how their optimal ratio varies across environments. By analysing two large, independent leaf-trait datasets for woody species 1,5 , we provide quantitative empirical support for the predicted dependencies of LL on LMA and environment in evergreen plants, and for the distinct predicted dependencies of LMA on light, temperature, growing-season length and aridity in evergreen and deciduous plants. We thereby resolve the long-standing question of why deciduous LMA tends to increase (with increasing LL) towards the equator, while evergreen LMA and LL decrease 6 . We also show how the statistical distribution of LMA within communities can be modelled as an outcome of environmental selection on the global pool of species with erse values of LMA and LL.
Publisher: Springer Science and Business Media LLC
Date: 27-03-2011
Publisher: Springer Science and Business Media LLC
Date: 30-05-2022
Publisher: Elsevier BV
Date: 05-2019
DOI: 10.1016/J.SCITOTENV.2019.02.293
Abstract: A clear interannual variability in annual production of grasslands (termed AEVI) has been reported over the Tibetan Plateau (TP), but the underlying mechanism has not been fully understood. Here, we explained the interannual variability of AEVI during 2001-2015 by two phenological metrics (the start and end of the growing season, termed SOS and EOS, respectively) and one physiological metric (the maximum capacity of canopy light absorbance, termed MEVI) using MODIS Enhanced Vegetation Index (EVI) data over the TP. The results showed that the interannual variability of AEVI can be well attributed to not only the trends of, but also the sensitivities of AEVI to, the selected biological metrics. On the one hand, the advancing SOS and delaying EOS dominated the study area while both increased and decreased MEVI were observed. On the other hand, the AEVI responded negatively to the SOS and positively to the EOS and MEVI, exhibiting significant variations along the temperature and precipitation gradients. Hence, the current interannual variability of SOS and EOS mainly increased the AEVI meanwhile, both enhancement and suppression of the interannual variability of MEVI to the AEVI were widespread over the TP. Overall, the interannual variability of MEVI mostly contributed to that of the AEVI, indicating a dominant role of the physiological metric rather than phenological metrics in carbon gain of TP grasslands. The achievements of this study are helpful to understand the underlying biological causes of the interannual variability of grassland production over the TP.
Publisher: IOP Publishing
Date: 22-10-2021
Abstract: Evaluation of potential crop yields is important for global food security assessment because it represents the biophysical ‘ceiling’ determined by variety, climate and ambient CO 2 . Statistical approaches have limitations when assessing future potential yields, while large differences between results obtained using process-based models reflect uncertainties in model parameterisations. Here we simulate the potential yield of wheat across the present-day wheat-growing areas, using a new global model that couples a parameter-sparse, optimality-based representation of gross primary production (GPP) to empirical functions relating GPP, biomass production and yield. The model reconciles the transparency and parsimony of statistical models with a mechanistic grounding in the standard model of C 3 photosynthesis, and seamlessly integrates photosynthetic acclimation and CO 2 fertilization effects. The model accurately predicted the CO 2 response observed in FACE experiments, and captured the magnitude and spatial pattern of EARTHSTAT ‘attainable yield’ data in 2000 CE better than process-based models in ISIMIP. Global simulations of potential yield during 1981–2016 were analysed in parallel with global historical data on actual yield, in order to test the hypothesis that environmental effects on modelled potential yields would also be shown in observed actual yields. Higher temperatures are thereby shown to have negatively affected (potential and actual) yields over much of the world. Greater solar radiation is associated with higher yields in humid regions, but lower yields in semi-arid regions. Greater precipitation is associated with higher yields in semi-arid regions. The effect of rising CO 2 is reflected in increasing actual yield, but trends in actual yield are stronger than the CO 2 effect in many regions, presumably because they also include effects of crop breeding and improved management. We present this hybrid modelling approach as a useful addition to the toolkit for assessing global environmental change impacts on the growth and yield of arable crops.
Publisher: Springer Science and Business Media LLC
Date: 14-07-2017
DOI: 10.1038/NCOMMS16137
Abstract: Nature Communications 7: Article number:13428 (2017) Published 8 November 2016 Updated 14 July 2017 An earlier publication by Leggett and Ball presented statistical evidence for a relationship between the pause in global temperature, a pause in the global rate of change of CO2 and an increase in global vegetation cover.
Publisher: MDPI AG
Date: 04-12-2018
DOI: 10.3390/F9120754
Abstract: Climate change is likely to lead to an increased frequency of droughts and floods, both of which are implicated in large-scale carbon allocation and tree mortality worldwide. Non-structural carbohydrates (NSCs) play an important role in tree survival under stress, but how NSC allocation changes in response to drought or waterlogging is still unclear. We measured soluble sugars (SS) and starch in leaves, twigs, stems and roots of Robinia pseudoacacia L. seedlings that had been subjected to a gradient in soil water availability from extreme drought to waterlogged conditions for a period of 30 days. Starch concentrations decreased and SS concentrations increased in tissues of R. pseudoacacia seedlings, such that the ratio of SS to starch showed a progressive increase under both drought and waterlogging stress. The strength of the response is asymmetric, with the largest increase occurring under extreme drought. While the increase in SS concentration in response to extreme drought is the largest in roots, the increase in the ratio of SS to starch is the largest in leaves. In idual components of SS showed different responses to drought and waterlogging across tissues: glucose concentrations increased significantly with drought in all tissues but showed little response to waterlogging in twigs and stems sucrose and fructose concentrations showed marked increases in leaves and roots in response to drought but a greater response to drought and waterlogging in stems and twigs. These changes are broadly compatible with the roles of in idual SS under conditions of water stress. While it is important to consider the role of NSC in buffering trees against mortality under stress, modelling this behaviour is unlikely to be successful unless it accounts for different responses within organs and the type of stress involved.
Publisher: Wiley
Date: 21-07-2021
DOI: 10.1111/NPH.17558
Abstract: Global vegetation and land‐surface models embody interdisciplinary scientific understanding of the behaviour of plants and ecosystems, and are indispensable to project the impacts of environmental change on vegetation and the interactions between vegetation and climate. However, systematic errors and persistently large differences among carbon and water cycle projections by different models highlight the limitations of current process formulations. In this review, focusing on core plant functions in the terrestrial carbon and water cycles, we show how unifying hypotheses derived from eco‐evolutionary optimality (EEO) principles can provide novel, parameter‐sparse representations of plant and vegetation processes. We present case studies that demonstrate how EEO generates parsimonious representations of core, leaf‐level processes that are in idually testable and supported by evidence. EEO approaches to photosynthesis and primary production, dark respiration and stomatal behaviour are ripe for implementation in global models. EEO approaches to other important traits, including the leaf economics spectrum and applications of EEO at the community level are active research areas. Independently tested modules emerging from EEO studies could profitably be integrated into modelling frameworks that account for the multiple time scales on which plants and plant communities adjust to environmental change.
Publisher: Cold Spring Harbor Laboratory
Date: 27-02-2023
DOI: 10.1101/2023.02.25.529932
Abstract: Vegetation cover regulates the exchanges of energy, water and carbon between land and atmosphere. Remotely-sensed fractional absorbed photosynthetically active radiation (fAPAR), a land-surface greenness metric, depends on carbon allocation to foliage while also controlling photon flux for photosynthesis. Greenness is thus both a driver and an outcome of gross primary production (GPP). An equation with just two (globally) fitted parameters describes annual maximum fAPAR (fAPAR max ) as the smaller of a water-limited value, transpiring a constant fraction of annual precipitation, and an energy-limited value, maximizing annual plant growth. This minimalist description reproduces global greenness patterns, and the consistent temporal trends among remote-sensing products, as accurately as the best-performing dynamic global vegetation models. Widely observed greening is attributed to the influence of rising carbon dioxide on the light- and water-use efficiencies of GPP, augmented by wetting in some dry regions and warming in high latitudes. Limited regions show browning, attributed to drying.
Publisher: Springer Science and Business Media LLC
Date: 08-12-2021
DOI: 10.1038/S41586-021-04096-9
Abstract: The global terrestrial carbon sink is increasing
Publisher: Elsevier BV
Date: 06-2020
Publisher: Wiley
Date: 09-07-2020
DOI: 10.1111/NPH.16702
Start Date: 2015
End Date: 2016
Funder: China Postdoctoral Science Foundation
View Funded ActivityStart Date: 2017
End Date: 2019
Funder: National Natural Science Foundation of China
View Funded ActivityStart Date: 07-2022
End Date: 12-2025
Amount: $584,995.00
Funder: Australian Research Council
View Funded Activity