Publications
433 results found
Joshi J, Stocker B, Hofhansl F, et al., 2022, Towards a unified theory of plant photosynthesis and hydraulics, Nature Plants, Vol: 8, Pages: 1304-1316, ISSN: 2055-026X
The global carbon and water cycles are governed by the coupling of CO2 and water vapour exchanges through the leaves of terrestrial plants, controlled by plant adaptations to balance carbon gains and hydraulic risks. We introduce a trait-based optimality theory that unifies the treatment of stomatal responses and biochemical acclimation of plants to environments changing on multiple timescales. Tested with experimental data from 18 species, our model successfully predicts the simultaneous decline in carbon assimilation rate, stomatal conductance and photosynthetic capacity during progressive soil drought. It also correctly predicts the dependencies of gas exchange on atmospheric vapour pressure deficit, temperature and CO2. Model predictions are also consistent with widely observed empirical patterns, such as the distribution of hydraulic strategies. Our unified theory opens new avenues for reliably modelling the interactive effects of drying soil and rising atmospheric CO2 on global photosynthesis and transpiration.
Nobrega R, Prentice IC, 2022, Holistic analysis of the carbon and water cycles to quantify the human footprint in basin-wide hydrological processes in the Amazon
<jats:p>&lt;p&gt;While land-cover clearing (LCC) immediately reduces evapotranspiration (ET), its effects on other water fluxes, such as river discharge and terrestrial water storage, exhibit contrasting responses depending on location and scale. One explanation for this is that LCC triggers a series of asynchronous disruptions in the equilibrium of hydrological processes that was established upon the long-term balance with regional climatological, edaphic, and geological characteristics. Water fluxes under these circumstances are not well represented by hydrological models that have Budyko-like approaches or rely on the stationarity of the hydrological responses. The complexity of such analysis is incremented once LCC is followed by the conversion to pastures and crops established over random spatial and temporal patterns throughout river basins. Here, we propose an analysis of river discharge and root zone storage capacity (RZSC) to unveil underlying relationships between stream dynamics and water consumption by plants. We use a time-series segmentation and residual trend analysis on streamflow and precipitation of high-order tributaries of the Tapaj&amp;#243;s River in the Amazon whose catchments underwent an intense land-use change over the past decades. We estimate the RZSC using the mass-curve balance method by considering the annual land-cover changes over a &gt;30-year period. Despite the common belief that increases in river discharge are primarily caused by reduced ET when precipitation trends are not significant, we show that this might not be the main trigger of streamflow change in these major Amazon catchments. Instead, the reduction in the RZSC caused by changes in the water consumption by plants over the dry season is tightly associated with the increased baseflow contribution to rivers. Finally, we analysed gross primary productivity (GPP) and ET estimates generated by a model based on eco-evolutionary optimalit
Chen JM, Wang R, Liu Y, et al., 2022, Global datasets of leaf photosynthetic capacity for ecological and earth system research, Earth System Science Data, Vol: 14, Pages: 4077-4093, ISSN: 1866-3508
The maximum rate of Rubisco carboxylation (Vcmax) determines leaf photosynthetic capacity and is a keyparameter for estimating the terrestrial carbon cycle, but its spatial information is lacking, hindering global ecologicalresearch. Here, we convert leaf chlorophyll content (LCC) retrieved from satellite data to Vcmax, based on plants’ optimaldistribution of nitrogen between light harvesting and carboxylation pathways. We also derive Vcmax from satellite (GOME-2)observations of sun-induced chlorophyll fluorescence (SIF) as a proxy of leaf photosynthesis using a data assimilationtechnique. These two independent global Vcmax products agree well (r2=0.79, RMSE=15.46 μmol m-2s-1 25 , P<0.001) andcompare well with 3672 ground-based measurements (r2=0.68, RMSE=13.55 μmol m-2s-1and P<0.001 for SIF; r2=0.55,RMSE=17.55 μmol m-2s-1 and P<0.001 for LCC). The LCC-derived Vcmax product is also used to constrain the retrieval ofVcmax from TROPOMI SIF data to produce an optimized Vcmax product using both SIF and LCC information. The globaldistributions of these products are compatible with Vcmax computed from an ecological optimality theory using meteorological variables, but importantly reveal additional information on the influence of land cover, irrigation, soil pH andleaf nitrogen on leaf photosynthetic capacity. These satellite-based approaches and spatial Vcmax products are primed to play amajor role in global ecosystem research. The three remote sensing Vcmax products based on SIF, LCC and SIF+LCC areavailable at https://doi.org/10.5281/zenodo.6466968 (Chen et al., 2020) and the code for implementing the ecologicaloptimality theory is available at https://github.com/SmithEcophysLab/optimal_vcmax_R (Smith, 2020).
Cheng S, Prentice IC, Huang Y, et al., 2022, Data-driven surrogate model with latent data-assimilation: application to wildfire forecasting, Journal of Computational Physics, Vol: 464, ISSN: 0021-9991
The large and catastrophic wildfires have been increasing across the globe in the recent decade, highlighting the importance of simulating and forecasting fire dynamics in near real-time. This is extremely challenging due to the complexities of physical models and geographical features. Running physics-based simulations for large wildfire events in near real-time are computationally expensive, if not infeasible. In this work, we develop and test a novel data-model integration scheme for fire progression forecasting, that combines Reduced-order modelling, recurrent neural networks (Long-Short-Term Memory), data assimilation, and error covariance tuning. The Reduced-order modelling and the machine learning surrogate model ensure the efficiency of the proposed approach while the data assimilation enables the system to adjust the simulation with observations. We applied this algorithm to simulate and forecast three recent large wildfire events in California from 2017 to 2020. The deep-learning-based surrogate model runs around 1000 times faster than the Cellular Automata simulation which is used to generate training data-sets. The daily fire perimeters derived from satellite observation are used as observation data in Latent Assimilation to adjust the fire forecasting in near real-time. An error covariance tuning algorithm is also performed in the reduced space to estimate prior simulation and observation errors. The evolution of the averaged relative root mean square error (R-RMSE) shows that data assimilation and covariance tuning reduce the RMSE by about 50% and considerably improves the forecasting accuracy. As a first attempt at a reduced order wildfire spread forecasting, our exploratory work showed the potential of data-driven machine learning models to speed up fire forecasting for various applications.
Dong N, Wright IJ, Chen JM, et al., 2022, Rising CO2 and warming reduce global canopy deman for nitrogen, New Phytologist, Vol: 235, Pages: 1692-1700, ISSN: 0028-646X
Nitrogen (N) limitation has been considered as a constraint on terrestrial carbon uptake in response to rising CO2 and climate change. By extension, it has been suggested that declining carboxylation capacity (Vcmax) and leaf N content in enhanced-CO2 experiments and satellite records signify increasing N limitation of primary production. We predicted Vcmax using the coordination hypothesis, and estimated changes in leaf-level photosynthetic N for 1982–2016 assuming proportionality with leaf-level Vcmax at 25˚C. Whole-canopy photosynthetic N was derived using satellite-based leaf area index (LAI) data and an empirical extinction coefficient for Vcmax, and converted to annual N demand using estimated leaf turnover times. The predicted spatial pattern of Vcmax 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 CO2, 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.
Liu M, Prentice IC, Menviel L, et al., 2022, Past rapid warmings as a constraint on greenhouse-gas climate feedbacks, Communications Earth & Environment, Vol: 3, ISSN: 2662-4435
There are large uncertainties in the estimation of greenhouse-gas climate feedback. Recent observations do not provide strong constraints because they are short and complicated by human interventions, while model-based estimates differ considerably. Rapid climate changes during the last glacial period (Dansgaard-Oeschger events), observed near-globally, were comparable in both rate and magnitude to current and projected 21st century climate warming and therefore provide a relevant constraint on feedback strength. Here we use these events to quantify the centennial-scale feedback strength of CO2, CH4 and N2O by relating global mean temperature changes, simulated by an appropriately forced low-resolution climate model, to the radiative forcing of these greenhouse gases derived from their concentration changes in ice-core records. We derive feedback estimates (expressed as dimensionless gain) of 0.14 ± 0.04 for CO2, 0.10 ± 0.02 for CH4, and 0.09 ± 0.03 for N2O. This indicates that much lower or higher estimates of gains, particularly some previously published values for CO2, are unrealistic.
Cheng S, Jin Y, Harrison S, et al., 2022, Parameter flexible wildfire prediction using machine learning techniques: forward and inverse modelling, Remote Sensing, Vol: 14, ISSN: 2072-4292
Parameter identification for wildfire forecasting models often relies on case-by-case tuning or posterior diagnosis/analysis, which can be computationally expensive due to the complexity of the forward prediction model. In this paper, we introduce an efficient parameter flexible fire prediction algorithm based on machine learning and reduced order modelling techniques. Using a training dataset generated by physics-based fire simulations, the method forecasts burned area at different time steps with a low computational cost. We then address the bottleneck of efficient parameter estimation by developing a novel inverse approach relying on data assimilation techniques (latent assimilation) in the reduced order space. The forward and the inverse modellings are tested on two recent large wildfire events in California. Satellite observations are used to validate the forward prediction approach and identify the model parameters. By combining these forward and inverse approaches, the system manages to integrate real-time observations for parameter adjustment, leading to more accurate future predictions.
Cruz-Silva E, Harrison SP, Marinova-Wolff E, et al., 2022, A new method based on surface- sample pollen data for reconstructing palaeovegetation patterns., Journal of Biogeography, Vol: 49, Pages: 1381-1396, ISSN: 0305-0270
Aim: Amongst the various techniques available to reconstruct past vegetation at regional to continental scales, biomisation has been the most widely used because it does not require an extensive modern pollen data set. However, it has well well-known limitations including its dependence on expert judgement for the assignment of pollen taxa to plant functional types (PFTs) and PFTs to biomes. Here we present a new method that combines the strengths of biomisation with those of the alternative dissimilarity-based techniques. This new technique quantifies the likelihood that a sample belongs to a given biome, and allows discrimination of non-analogue vegetation types. Location: The Eastern Mediterranean-Black Sea Caspian Corridor (EMBSeCBIO) region, 28°-49°N, 20°- 62°E. Methods: Modern pollen samples assigned to biomes based on potential natural vegetation data, are used to characterize biomes according to the within-biome means and standard deviations of the abundances of each taxon. These are used to calculate a dissimilarity index between any given pollen sample and every biome, and thus assign a pollen sample to the most likely biome. We also calculate a threshold value for each biome which identifies samples that fall outside the acceptable range of likelihoods for biome assignment and hence can be used to distinguish non-analogue vegetation. We have applied the new technique to the EMBSeCBIO region to compare the performance of the new method with existing reconstructions. Results: The technique captured changes in the importance of individual taxa along environmental gradients. The balanced accuracy obtained for the EMBSeCBIO region using the new method was better than that obtained using biomisation (77% versus 65%). When the method was applied to high resolution fossil records, 70% of the evaluated entities showed more temporally stable biome assignments than obtained with the biomisation method. The technique also identifies likely non analogu
Cheng S, Jin Y, Harrison SP, et al., 2022, Parameter flexible wildfire prediction using machine learning techniques:forward and inverse modelling, Remote Sensing, ISSN: 2072-4292
Keenan TFC, Luo X, De Kauwe MG, et al., 2022, A constraint on historic growth in global photosynthesis due to increasing CO<sub>2</sub> (Retraction of Vol 600, Pg 253, 2021), NATURE, Vol: 606, Pages: 420-420, ISSN: 0028-0836
Wang H, Wang R, Harrison SP, et al., 2022, Leaf morphological traits as adaptations to multiple climate gradients, Journal of Ecology, Vol: 110, Pages: 1344-1355, ISSN: 0022-0477
1. 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.2. A data set 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 characterize co-occurring trait syndromes (k-means clustering) and their climatic preferences. 3. Three axes accounted for > 20% of trait variation in both evergreen and deciduous species. Moisture index, precipitation seasonality and growing-season temperature accounted for 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 example 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 notop
Shen Y, Sweeney L, Liu M, et al., 2022, Reconstructing burnt area during the Holocene: an Iberian case study, Climate of the Past, Vol: 18, Pages: 1189-1201, ISSN: 1814-9324
Charcoal accumulated in lake, bog or other anoxic sediments through time has been used to document the geographical patterns in changes in fire regimes. Such reconstructions are useful to explore the impact of climate and vegetation changes on fire during periods when human influence was less prevalent than today. However, charcoal records only provide semi-quantitative estimates of change in biomass burning. Here we derive quantitative estimates of burnt area from vegetation data in two stages. First, we relate the modern charcoal abundance to burnt area using a conversion factor derived from a generalised linear model of burnt area probability based on eight environmental predictors. Then, we establish the relationship between fossil pollen assemblages and burnt area using tolerance-weighted weighted averaging partial least-squares regression with a sampling frequency correction (fxTWA-PLS). We test this approach using the Iberian Peninsula as a case study because it is a fire-prone region with abundant pollen and charcoal records covering the Holocene. We derive the vegetation–burnt area relationship using the 31 records that have both modern and fossil charcoal and pollen data and then reconstruct palaeoburnt area for the 113 records with Holocene pollen records. The pollen data predict charcoal-derived burnt area relatively well (R2 = 0.44), and the changes in reconstructed burnt area are synchronous with known climate changes through the Holocene. This new method opens up the possibility of reconstructing changes in fire regimes quantitatively from pollen records, after regional calibration of the vegetation–burnt area relationship, in regions where pollen records are more abundant than charcoal records.
Haas O, Prentice IC, Harrison SP, 2022, Global environmental controls of wildfire burnt area, size and intensity., Environmental Research Letters, Vol: 17, Pages: 1-12, ISSN: 1748-9326
Fire is an important influence on the global patterns of vegetation structure and composition. Wildfire is included as a distinct process in many dynamic global vegetation models but limited current understanding of fire regimes restricts these models' ability to reproduce more than the broadest geographic patterns. Here we present a statistical analysis of the global controls of remotely sensed burnt area (BA), fire size (FS), and a derived metric related to fire intensity (FI). Separate generalized linear models were fitted to observed monthly fractional BA from the Global Fire Emissions Database (GFEDv4), median FS from the Global Fire Atlas, and median fire radiative power from the MCD14ML dataset normalized by the square root of median FS. The three models were initially constructed from a common set of 16 predictors; only the strongest predictors for each model were retained in the final models. It is shown that BA is primarily driven by fuel availability and dryness; FS by conditions promoting fire spread; and FI by fractional tree cover and road density. Both BA and FS are constrained by landscape fragmentation, whereas FI is constrained by fuel moisture. Ignition sources (lightning and human population) were positively related to BA (after accounting for road density), but negatively to FI. These findings imply that the different controls on BA, FS and FI need to be considered in process-based models. They highlight the need to include measures of landscape fragmentation as well as fuel load and dryness, and to pay close attention to the controls of fire spread.
Prentice IC, Villegas-Diaz R, Harrison SP, 2022, Accounting for atmospheric carbon dioxide variations in pollen-based reconstructions of past hydroclimates., Global and Planetary Change, Vol: 211, Pages: 1-9, ISSN: 0921-8181
Changes in atmospheric carbon dioxide (CO2) concentration directly influence the ratio of stomatal water loss to carbon uptake. This ratio (e) is a fundamental quantity for terrestrial ecosystems, as it defines the water requirement for plant growth. Statistical and analogue-based methods used to reconstruct past hydroclimate variables from fossil pollen assemblages do not take account of the effect of CO2 variations on e. Here we present a general, globally applicable method to correct for this effect. The method involves solving an equation that relates e to a climatic moisture index (MI, the ratio of mean annual precipitation to mean annual potential evapotranspiration), mean growing-season temperature, and ambient CO2. The equation is based on the least-cost optimality hypothesis, which predicts how the ratio (χ) of leaf-internal to ambient CO2 varies with vapour pressure deficit (vpd), growing-season temperature and atmospheric pressure, combined with experimental evidence on the response of χ to the CO2 level at which plants have been grown. An empirical relationship based on global climate data is used to relate vpd to MI and growing-season temperature. The solution to the equation allows past MI to be estimated from pollen-reconstructed MI, given past CO2 and temperature. This MI value can be used to estimate mean annual precipitation, accounting for the effects of orbital variations, temperature and cloud cover (inferred from MI) on potential evapotranspiration. A pollen record from semi-arid Spain that spans the last glacial interval is used to illustrate the method. Low CO2 leads to estimated MI being larger than reconstructed MI during glacial times. The CO2 effect on inferred precipitation was partly offset by increased cloud cover; nonetheless, inferred precipitation was greater than present almost throughout the glacial period. This method allows a more robust reconstruction of past hydroclimatic variations than currently available tools.
Gan W, Nóbrega R, Prentice IC, 2022, Analysis of vegetation modelling uncertainties due to soil moisture stress during droughts
<jats:p>&lt;p&gt;Many model uncertainties results from parameter tuning to compensate for errors in model outputs. A number of studies have focused on the analysis of uncertainties in modelled gross primary production (GPP), particularly with regard to the representation of soil moisture stress. GPP is often overestimated by models during dry periods in water-limited regions, and this bias increases during drought events. Soil moisture stress functions are widely applied to correct this. However, soil moisture stress is not always the direct constraining factor on GPP, and the functions adopted by models do not correspond to accepted mechanisms. We have used eco-evolutionary optimality principles, via the so-called P model, to estimate carbon uptake at sites where leaf area index (LAI) was routinely measured. We used observational networks (including FLUXNET) and Fraction of Absorbed Photosynthetically Active Radiation (fAPAR) data from satellites. By comparing modelled and observed GPP we determined whether there is a significant difference between model performance during the dry and wet seasons, or between energy- and water-limited sites. We found that the soil moisture stress function used in one version of the P models essentially compensates for uncertainties in fAPAR data from satellites, especially in grasslands and other areas subject to seasonal drought. This situation is problematic, since soil moisture is a driver or modulator of other ecosystem processes, including soil evaporation and runoff generation. A possible way forward involves implementing phenological components dependent on soil and atmospheric conditions. The new challenge this poses is to apply eco-evolutionary optimality principles to model the seasonal time course of LAI, which is often poorly simulated by complex ecosystem models.&lt;/p&gt;</jats:p>
Nóbrega R, Prentice IC, 2022, Rapid and temporary increases in low flows in the Amazon explained by changes in root-zone water storage
<jats:p>&lt;p&gt;Increases in streamflow are often attributed to land-cover clearing (LCC) on the basis that it reduces soil infiltration capacity and increases surface runoff. Nonetheless, these changes can result from different hydrological mechanisms depending on the vegetation, and temporal and spatial scales. LCC triggers a series of changes in hydrological fluxes that have non-linear responses to precipitation and that were established upon the long-term balance with regional climatological, edaphic, and geological characteristics. We analysed streamflow and root zone water capacity (RZSC) to identify underlying relationships between stream dynamics and water consumption by plants. We used a time-series segmentation and residual trend analysis on streamflow and precipitation of high-order tributaries of the Tapaj&amp;#243;s River whose catchments underwent intense land-use changes over the past decades. We estimated the RZSC using the &quot;Earth observation-based&quot; mass-curve balance method by considering the annual land-cover changes over a &gt;30-year period. We show that the reduction in the RZWC caused by changes in the water consumption by plants over the dry season is tightly associated with the increased baseflow contribution to rivers. Finally, we analysed gross primary productivity (GPP) and ET estimates generated by a model based on eco-evolutionary optimality that integrates the water and carbon cycles at the canopy level. We found that trends in ET from croplands are not as pronounced as trends in GPP. Although RZWC is quantified using the water deficit driven by ET, changes in RZWC are more correlated to changes in GPP. We show that the potential effects of vegetation responses to increasing atmospheric CO&lt;sub&gt;2&lt;/sub&gt; concentrations on streamflow are still outweighed by impacts of land-use change on low flows in Amazon rivers. However, this might
Lavergne A, Harrison SP, Prentice IC, 2022, Investigating C3/C4 plants competition using carbon isotopes and optimality principles
<jats:p>&lt;p&gt;Understanding the mechanisms underlying changes in carbon isotope discrimination (&amp;#916;&lt;sup&gt;13&lt;/sup&gt;C) in C&lt;sub&gt;3&lt;/sub&gt; and C&lt;sub&gt;4&lt;/sub&gt; plants is critical for predicting the C&lt;sub&gt;3&lt;/sub&gt;/C&lt;sub&gt;4&lt;/sub&gt; fraction in mixed ecosystems. Variations in &amp;#916;&lt;sup&gt;13&lt;/sup&gt;C are closely related to changes in the stomatal limitation of photosynthesis (i.e. the ratio of leaf internal to ambient partial pressure of CO&lt;sub&gt;2&lt;/sub&gt;, &lt;em&gt;c&lt;/em&gt;&lt;sub&gt;i&lt;/sub&gt;/&lt;em&gt;c&lt;/em&gt;&lt;sub&gt;a&lt;/sub&gt;), which are in turn determined by environmental variables, but also depend on the pathway of carbon assimilation. For instance, isotopic fractionation during the diffusion of CO&lt;sub&gt;2&lt;/sub&gt; through the stomata primarily influences &amp;#916;&lt;sup&gt;13&lt;/sup&gt;C in C&lt;sub&gt;4&lt;/sub&gt; plants, while fractionation during Rubisco carboxylation has a stronger imprint on &amp;#916;&lt;sup&gt;13&lt;/sup&gt;C in C&lt;sub&gt;3&lt;/sub&gt; plants. As a result, C&lt;sub&gt;3&lt;/sub&gt; plants are depleted in &lt;sup&gt;13&lt;/sup&gt;C compared to C&lt;sub&gt;4&lt;/sub&gt; plants. Isotopic measurements can thus be used as tracers of physiological processes in plants.&lt;/p&am
Mengoli G, Harrison SP, Prentice IC, 2022, Towards a land surface model based on optimality principles
<jats:p>&lt;p&gt;Plants take up water from the soil via roots and release it into the atmosphere through stomata; uptake of CO&lt;sub&gt;2&lt;/sub&gt; from the atmosphere also proceeds through the stomata, implying tight coupling of transpiration and photosynthesis. We distinguish leaf-level (biochemical and stomatal) responses to external stimuli on different timescales: fast responses taking place over seconds to hours, and longer-term (acclimation) responses taking place over weeks to months. Typically, land-surface models (LSMs) have focused on the fast responses, and have not accounted for acclimation responses, although these can be different in magnitude and even in sign. We have developed a method that explicitly separates these two timescales in order to implement an existing optimality-based model, the P model, with a sub-daily timestep; and, thereby, to include acclimated responses within an LSM framework. The resulting model, compared to flux-tower gross primary production (GPP) data in five &amp;#8220;well-watered&amp;#8221; biomes from boreal to tropical, correctly reproduces diurnal cycles of GPP throughout the growing season. No changes of parameters are required between biomes, because optimality ensures that current parameter values are always adapted to the local environment. This is a clear practical advantage because it eliminates the need to specify different parameter values for different plant functional types. However, in areas with large seasonal variations in moisture variability, the model does not perform well. Here we address the issue of soil-moisture controls on GPP, which is a challenging issue for LSMs in general. We note two problems: an error in magnitude, and an error in shape. The model tends to overestimate GPP in dry areas because it does not consider the effect of low soil moisture (as opposed to atmospheric dryness) on photosynthesis; and it does not simul
Chen C, Riley W, Prentice IC, et al., 2022, CO2 fertilization of terrestrial photosynthesis inferred from site to global scales, Proceedings of the National Academy of Sciences of USA, Vol: 119, ISSN: 0027-8424
Global photosynthesis is increasing with elevated atmospheric CO2 concentrations, a response known as the CO2 fertilization effect (CFE), but the key processes of CFE are not constrained and therefore remain uncertain. Here we quantify CFE by combining observations from a globally distributed network of eddy covariance measurements with a novel analyticalframework based on three well-established photosynthetic optimization theories. We report a strong enhancement of photosynthesis across the observational network (9.1 gC m–2 yr–2) and show that the CFE is responsible for 44% of the gross primary production (GPP) enhancementsince the 2000s, with additional contributions primarily from warming (28%). Soil moisture and specific humidity are the two largest contributors to GPP interannual variation through their influences on plant hydraulics. Applying our framework to satellite observations and meteorological reanalysis data, we diagnose a global CO2-induced GPP trend of 4.4 gC m–2 yr–2, which is at least one-third stronger than the median trends of 13 Dynamic Global Vegetation Models and 8 satellite-derived GPP products, mainly due to their differences in the magnitude of CFE in evergreen broadleaf forests. These results highlight the critical role that CFE has had on the global carbon cycle in recent decades.
Morfopoulos C, Muller J-F, Stavrakou T, et al., 2022, Vegetation responses to climate extremes recorded by remotely sensed atmospheric formaldehyde, Global Change Biology, Vol: 28, Pages: 1809-1822, ISSN: 1354-1013
Accurate monitoring of vegetation stress is required for better modelling and forecasting of primary production, in a world where heatwaves and droughts are expected to become increasingly prevalent. Variability in formaldehyde (HCHO) concentrations in the troposphere is dominated by local emissions of short-lived biogenic (BVOC) and pyrogenic volatile organic compounds. BVOCs are emitted by plants in a rapid protective response to abiotic stress, mediated by the energetic status of leaves (the excess of reducing power when photosynthetic light and dark reactions are decoupled, as occurs when stomata close in response to water stress). Emissions also increase exponentially with leaf temperature. New analytical methods for the detection of spatiotemporally contiguous extremes in remote-sensing data are applied here to satellite-derived atmospheric HCHO columns. BVOC emissions are shown to play a central role in the formation of the largest positive HCHO anomalies. Although vegetation stress can be captured by various remotely sensed quantities, spaceborne HCHO emerges as the most consistent recorder of vegetation responses to the largest climate extremes, especially in forested regions.
Fu Z, Ciais P, Prentice IC, et al., 2022, Atmospheric dryness reduces photosynthesis along a large range of soil water deficits, Nature Communications, Vol: 13, ISSN: 2041-1723
Both low soil water content (SWC) and high atmospheric dryness (vapor pressure deficit, VPD) can negatively affect terrestrial gross primary production (GPP). The sensitivity of GPP to soil versus atmospheric dryness is difficult to disentangle, however, because of their covariation. Using global eddy-covariance observations, here we show that a decrease in SWC is not universally associated with GPP reduction. GPP increases in response to decreasing SWC when SWC is high and decreases only when SWC is below a threshold. By contrast, the sensitivity of GPP to an increase of VPD is always negative across the full SWC range. We further find canopy conductance decreases with increasing VPD (irrespective of SWC), and with decreasing SWC on drier soils. Maximum photosynthetic assimilation rate has negative sensitivity to VPD, and a positive sensitivity to decreasing SWC when SWC is high. Earth System Models underestimate the negative effect of VPD and the positive effect of SWC on GPP such that they should underestimate the GPP reduction due to increasing VPD in future climates.
Mengoli G, Agusti-Panareda A, Boussetta S, et al., 2022, Ecosystem photosynthesis in land-surface models: a first-principles approach incorporating acclimation, Journal of Advances in Modeling Earth Systems, Vol: 14, Pages: 1-18, ISSN: 1942-2466
Vegetation regulates land-atmosphere water and energy exchanges and is an essential component of land-surface models (LSMs). However, LSMs have been handicapped by assumptions that equate acclimated photosynthetic responses to the environment with the fast responses observable in the laboratory. The effects of acclimation can be taken into account by including PFT-specific values of photosynthetic parameters, but at the cost of increasing parameter requirements. Here we develop an alternative approach for including acclimation in LSMs by adopting the P model, an existing light-use efficiency model for gross primary production (GPP) that implicitly predicts the acclimation of photosynthetic parameters on a weekly to monthly timescale via optimality principles. We demonstrate that it is possible to explicitly separate the fast and slow photosynthetic responses to environmental conditions, allowing the simulation of GPP at the sub-daily timesteps required for coupling in an LSM. The resulting model reproduces the diurnal cycles of GPP recorded by eddy-covariance flux towers in a temperate grassland and boreal, temperate and tropical forests. The best performance is achieved when biochemical capacities are adjusted to match recent midday conditions. Comparison between this model and the operational LSM in the European Centre for Medium-range Weather Forecasts climate model shows that the new model has better predictive power in most of the sites and years analysed, particularly in summer and autumn. Our analyses suggest a simple and parameter-sparse method to include both instantaneous and acclimated responses within an LSM framework, with potential applications in weather, climate and carbon-cycle modelling.
Lavergne A, Hemming D, Prentice IC, et al., 2022, Global decadal variability of plant carbon isotope discrimination and its link to gross primary production., Global Change Biology, Vol: 28, Pages: 524-541, ISSN: 1354-1013
Carbon isotope discrimination (Δ13C) in C3 woody plants is a key variable for the study of photosynthesis. Yet how Δ13C varies at decadal scales, and across regions, and how it is related to gross primary production (GPP), are still incompletely understood. Here we address these questions by implementing a new Δ13C modelling capability in the land-surface model JULES incorporating both photorespiratory and mesophyll-conductance fractionations. We test the ability of four leaf-internal CO2 concentration models embedded in JULES to reproduce leaf and tree-ring (TR) carbon isotopic data. We show that all the tested models tend to overestimate average Δ13C values, and to underestimate interannual variability in Δ13C. This is likely because they ignore the effects of soil water stress on stomatal behavior. Variations in post-photosynthetic isotopic fractionations across species, sites and years, may also partly explain the discrepancies between predicted and TR-derived Δ13C values. Nonetheless, the “least-cost” (Prentice) model shows the lowest biases with the isotopic measurements, and lead to improved predictions of canopy-level carbon and water fluxes. Overall, modelled Δ13C trends vary strongly between regions during the recent (1979–2016) historical period but stay nearly constant when averaged over the globe. Photorespiratory and mesophyll effects modulate the simulated global Δ13C trend by 0.0015 ± 0.005‰ and –0.0006 ± 0.001‰ ppm−1, respectively. These predictions contrast with previous findings based on atmospheric carbon isotope measurements. Predicted Δ13C and GPP tend to be negatively correlated in wet-humid and cold regions, and in tropical African forests, but positively related elsewhere. The negative correlation between Δ13C and GPP is partly due to the strong dominant influences of temperature on GPP and vapor pressure deficit on Δ13
Keenan T, Luo X, De Kauwe MG, et al., 2021, A constraint on historic growth in global photosynthesis due to rising CO2, Nature, Vol: 600, Pages: 253-258, ISSN: 0028-0836
The global terrestrial carbon sink is increasing1,2,3, offsetting roughly a third of anthropogenic CO2 released into the atmosphere each decade1, and thus serving to slow4 the growth of atmospheric CO2. It has been suggested that a CO2-induced long-term increase in global photosynthesis, a process known as CO2 fertilization, is responsible for a large proportion of the current terrestrial carbon sink4,5,6,7. The estimated magnitude of the historic increase in photosynthesis as result of increasing atmospheric CO2 concentrations, however, differs by an order of magnitude between long-term proxies and terrestrial biosphere models7,8,9,10,11,12,13. Here we quantify the historic effect of CO2 on global photosynthesis by identifying an emergent constraint14,15,16 that combines terrestrial biosphere models with global carbon budget estimates. Our analysis suggests that CO2 fertilization increased global annual photosynthesis by 11.85 ± 1.4%, or 13.98 ± 1.63 petagrams carbon (mean ± 95% confidence interval) between 1981 and 2020. Our results help resolve conflicting estimates of the historic sensitivity of global photosynthesis to CO2, and highlight the large impact anthropogenic emissions have had on ecosystems worldwide.
Harrison S, Prentice IC, Bloomfield K, et al., 2021, Understanding and modelling wildfire regimes: an ecological perspective., Environmental Research Letters, Vol: 16, Pages: 1-13, ISSN: 1748-9326
Recent extreme wildfire seasons in several regions have been associated with exceptionally hot, dry conditions, made more probable by climate change. Much research has focused on extreme fire weather and its drivers, but natural wildfire regimes—and their interactions with human activities—are far from being comprehensively understood. There is a lack of clarity about the 'causes' of wildfire, and about how ecosystems could be managed for the co-existence of wildfire and people. We present evidence supporting an ecosystem-centred framework for improved understanding and modelling of wildfire. Wildfire has a long geological history and is a pervasive natural process in contemporary plant communities. In some biomes, wildfire would be more frequent without human settlement; in others they would be unchanged or less frequent. A world without fire would have greater forest cover, especially in present-day savannas. Many species would be missing, because fire regimes have co-evolved with plant traits that resist, adapt to or promote wildfire. Certain plant traits are favoured by different fire frequencies, and may be missing in ecosystems that are normally fire-free. For example, post-fire resprouting is more common among woody plants in high-frequency fire regimes than where fire is infrequent. The impact of habitat fragmentation on wildfire crucially depends on whether the ecosystem is fire-adapted. In normally fire-free ecosystems, fragmentation facilitates wildfire starts and is detrimental to biodiversity. In fire-adapted ecosystems, fragmentation inhibits fires from spreading and fire suppression is detrimental to biodiversity. This interpretation explains observed, counterintuitive patterns of spatial correlation between wildfire and potential ignition sources. Lightning correlates positively with burnt area only in open ecosystems with frequent fire. Human population correlates positively with burnt area only in densely forested regions. Models for ve
Keenan TF, Luo X, De Kauwe MG, et al., 2021, A constraint on historic growth in global photosynthesis due to increasing CO2., Nature, Vol: 600, Pages: 253-258
The global terrestrial carbon sink is increasing1-3, offsetting roughly a third of anthropogenic CO2 released into the atmosphere each decade1, and thus serving to slow4 the growth of atmospheric CO2. It has been suggested that a CO2-induced long-term increase in global photosynthesis, a process known as CO2 fertilization, is responsible for a large proportion of the current terrestrial carbon sink4-7. The estimated magnitude of the historic increase in photosynthesis as result of increasing atmospheric CO2 concentrations, however, differs by an order of magnitude between long-term proxies and terrestrial biosphere models7-13. Here we quantify the historic effect of CO2 on global photosynthesis by identifying an emergent constraint14-16 that combines terrestrial biosphere models with global carbon budget estimates. Our analysis suggests that CO2 fertilization increased global annual photosynthesis by 11.85 ± 1.4%, or 13.98 ± 1.63 petagrams carbon (mean ± 95% confidence interval) between 1981 and 2020. Our results help resolve conflicting estimates of the historic sensitivity of global photosynthesis to CO2, and highlight the large impact anthropogenic emissions have had on ecosystems worldwide.
Xu H, Wang H, Prentice IC, et al., 2021, Coordination of plant hydraulic and photosynthetic traits: confronting optimality theory with field measurements, New Phytologist, Vol: 232, Pages: 1286-1296, ISSN: 0028-646X
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 are linked by the sapwood-to-leaf area ratio (Huber value, vH). The observed coordination between vH and sapwood hydraulic conductivity (KS) and photosynthetic capacity (Vcmax) conformed to the proposed quantitative theory. Substantial hydraulic diversity was related to the trade-off between KS and vH. Leaf drought tolerance (inferred from turgor loss point, –Ψtlp) increased with wood density, but the trade-off between hydraulic efficiency (KS) and –Ψtlp was weak. Plant trait effects on vH were dominated by variation in KS, 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.
Qiao S, Wang H, Prentice IC, et al., 2021, Optimality-based modelling of climate impacts on global potential wheat yield., Environmental Research Letters, Vol: 16, Pages: 1-13, ISSN: 1748-9326
Evaluation of potential crop yields is important for global food security assessment because it represents the biophysical 'ceiling' determined by variety, climate and ambient CO2. 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 C3 photosynthesis, and seamlessly integrates photosynthetic acclimation and CO2 fertilization effects. The model accurately predicted the CO2 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 CO2 is reflected in increasing actual yield, but trends in actual yield are stronger than the CO2 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 toolki
Tan S, Wang H, Prentice IC, et al., 2021, Land-surface evapotranspiration from a first-principles primary production model, Environmental Research Letters, Vol: 16, Pages: 1-11, ISSN: 1748-9326
Evapotranspiration (ET) links the water and carbon cycles in the atmosphere, hydrosphere, and biosphere. In this study, we develop an ET modelling framework based on the idea that the transpiration and carbon uptake are closely coupled, as predicted by the 'least-cost hypothesis' that canopy conductance acclimates to environmental variations. According to eco-evolutionary optimality theory, which has been previously applied in monitoring and modelling land-surface processes, the total costs (per unit carbon fixed) for maintaining transpiration and carboxylation capacities should be minimized. We calculate gross primary production (GPP) assuming that the light- and Rubisco-limited rates of photosynthesis, described by the classical biochemical model of photosynthesis, are coordinated on an approximately weekly time scale. Transpiration (T) is then calculated via acclimated canopy conductance, with no need for plant type- or biome-specific parameters. ET is finally calculated from T using an empirical function of light, temperature, soil water content and foliage cover to predict the T/ET ratio at each site. The GPP estimates were well supported by (weekly) GPP data at 20 widely distributed eddy-covariance flux sites (228 site-years), with correlation coefficients (r) = 0.81 and root-mean-square error (RMSE) = 18.7 gC week−1 and Nash-Sutcliffe efficiency (NSE) = 0.61. Predicted ET was also well supported, with r =0.85, RMSE = 5.5 mm week–1 and NSE = 0.66. Estimated T/ET ratios (0.43–0.74) showed significant positive relationships to radiation, precipitation and green vegetation cover and negative relationships to temperature and modelled T (r = 0.84). Aspects of this framework could be improved, notably the estimation of T/ET. Nonetheless, we see the application of eco-evolutionary principles as a promising direction for water resources research, eliminating the uncertainty introduced by the need to specify multiple parameters, and leveraging the pow
Daniel F, Prentice IC, Gallagher R, et al., 2021, AusTraits, a curated plant trait database for the Australian flora., Scientific Data, Vol: 8, Pages: 1-20, ISSN: 2052-4463
We introduce the AusTraits database - a compilation of values of plant traits for taxa in theAustralian flora (hereafter AusTraits). AusTraits synthesises data on 448 traits across28640 taxa from field campaigns, published literature, taxonomic monographs, andindividual taxon descriptions. Traits vary in scope from physiological measures ofperformance (e.g. photosynthetic gas exchange, water-use efficiency) to morphologicalattributes (e.g. leaf area, seed mass, plant height) which link to aspects of ecologicalvariation. AusTraits contains curated and harmonised individual- and species-levelmeasurements coupled to, where available, contextual information on site properties andexperimental conditions. This article provides information on version 3.0.2 of AusTraitswhich contains data for 997808 trait-by-taxon combinations. We envision AusTraits as anongoing collaborative initiative for easily archiving and sharing trait data and also providesa template for other national or regional initiatives globally to fill persistent gaps in traitknowledge.
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