358 results found
Prentice IC, Peng Y, Bloomfield K, et al., 2021, Global climate and nutrient controls of photosynthetic capacity, Communications Biology, ISSN: 2399-3642
Xu H, Wang H, Prentice IC, et al., 2021, Predictability of leaf traits with climate and elevation: a case study in Gongga Mountain, China, Tree Physiology: an international botanical journal, ISSN: 0829-318X
Leaf mass per area (Ma), nitrogen content per unit leaf area (Narea), maximum carboxylation capacity (Vcmax) and the ratio of leaf-internal to ambient CO2 partial pressure (χ) are important traits related to photosynthetic function, and show systematic variation along climatic and elevational gradients. Separating the effects of air pressure and climate along elevational gradients is challenging due to the covariation of elevation, pressure and climate. However, recently developed models based on optimality theory offer an independent way to predict leaf traits, and thus to separate the contributions of different controls. We apply optimality theory to predict variation in leaf traits across 18 sites in the Gongga Mountain region. We show that the models explain 59% of trait variability on average, without site- or region-specific calibration. Temperature, photosynthetically active radiation, vapor pressure deficit, soil moisture and growing-season length are all necessary to explain the observed patterns. The direct effect of air pressure is shown to have a relatively minor impact. These findings contribute to a growing body of research indicating that leaf-level traits vary with the physical environment in predictable ways, suggesting a promising direction for the improvement for terrestrial ecosystem models.
Turner M, Wei D, Prentice IC, et al., 2021, The impact of methodological decisions in climate reconstructions using WA- PLS, Quaternary Research, Vol: 99, Pages: 341-356, ISSN: 0033-5894
Most techniques for pollen-based quantitative climate reconstruction use modern assemblages as a reference data set. We examine the implication of methodological choices in the selection and treatment of the reference data set for climate reconstructions using Weighted Averaging Partial Least Squares (WA-PLS) regression, using records of the last glacial period from Europe. We show that the training data set used is important, because it determines the climate space sampled. The range and continuity of sampling along the climate gradient is more important than sampling density. Reconstruction uncertainties are generally reduced when more taxa are included, but combining related taxa that are poorly sampled in the data set to a higher taxonomic level provides more stable reconstructions. Excluding taxa that are climatically insensitive, or systematically over-represented in fossil pollen assemblages because of known biases in pollen production or transport, makes no significant difference to the reconstructions. However, the exclusion of taxa over-represented because of preservation issues does produce an improvement. These findings are relevant not only for WA-PLS reconstructions but also for similar approaches using modern assemblage reference data. There is no universal solution to these issues, but we propose a number of checks to evaluate the robustness of pollen-based reconstructions.
Prentice IC, Cai W, 2020, Recent trends in gross primary production and their drivers: analysis and modelling at flux-site and global scales, Environmental Research Letters, Vol: 15, ISSN: 1748-9326
Gross primary production (GPP) by terrestrial ecosystems is the largest flux in the global carbon cycle, and its continuing increase in response to environmental changes is key to land ecosystems' capacity to offset anthropogenic CO2 emissions. However, the CO2- and climate-sensitivities of GPP vary among models. We applied the 'P model'—a parameter-sparse and extensively tested light use efficiency (LUE) model, driven by CO2, climate and remotely sensed greenness data—at 29 sites with multi-year eddy-covariance flux measurements. Observed (both positive and negative) GPP trends at these sites were predicted, albeit with some bias. Increasing LUE (due to rising atmospheric CO2 concentration) and green vegetation cover were the primary controls of modelled GPP trends across sites. Global GPP simulated by the same model increased by 0.46 ± 0.09 Pg C yr–2 during 1982–2016. This increase falls in the mid-range rate of simulated increase by the TRENDY v8 ensemble of state-of-the-art ecosystem models. The modelled LUE increase during 1900–2013 was 15%, similar to a published estimate based on deuterium isotopomers. Rising CO2 was the largest contributor to the modelled GPP increase. Greening, which may in part be caused by rising CO2, ranked second but dominated the modelled GPP change over large areas, including semi-arid vegetation on all continents. Warming caused a small net reduction in modelled global GPP, but dominated the modelled GPP increase in high northern latitudes. These findings strengthen the evidence that rising LUE due to rising CO2 level and increased green vegetation cover (fAPAR) are the main causes of increasing GPP, and thereby, the terrestrial carbon sink.
Prentice IC, Liu M, ter Braak CJF, et al., 2020, An improved statistical approach for reconstructing past climates from biotic assemblages, Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences, Vol: 476, Pages: 1-21, ISSN: 1364-5021
Quantitative reconstructions of past climates are an important resource for evaluating how well climate models reproduce climate changes. One widely used statistical approach for making such reconstructions from fossil biotic assemblages is weighted averaging partial least-squares regression (WA-PLS). There is however a known tendency for WA-PLS to yield reconstructions compressed towards the centre of the climate range used for calibration, potentially biasing the reconstructed past climates. We present an improvement of WA-PLS by assuming that: (i) the theoretical abundance of each taxon is unimodal with respect to the climate variable considered; (ii) observed taxon abundances follow a multinomial distribution in which the total abundance of a sample is climatically uninformative; and (iii) the estimate of the climate value at a given site and time makes the observation most probable, i.e. it maximizes the log-likelihood function. This climate estimate is approximated by weighting taxon abundances in WA-PLS by the inverse square of their climate tolerances. We further improve the approach by considering the frequency ( fx) of the climate variable in the training dataset. Tolerance-weighted WA-PLS with fx correction greatly reduces the compression bias, compared with WA-PLS, and improves model performance in reconstructions based on an extensive modern pollen dataset.
Wei D, Gonzalez-Samperiz P, Gil-Romera G, et al., 2020, Seasonal temperature and moisture changes in interior semi-arid Spain from the last interglacial to the late Holocene, Quaternary Research, ISSN: 0033-5894
The El Cañizar de Villarquemado pollen record covers the last part of MIS 6 to the late Holocene. We use Tolerance-Weighted Averaging Partial Least-Squares (TWA-PLS) to reconstruct mean temperature of the coldest month (MTCO) and growing degree days above 0° C (GDD0) and the ratio of annual precipitation to annual potential evapotranspiration (MI), accounting for the ecophysiological effect of changing CO2 on water-use efficiency. Rapid summer warming occurred during the Zeifen-Kattegat Oscillation at the transition to MIS 5. Summers were cold during MIS 4 and MIS 2, but some intervals of MIS 3 had summers as warm as the warmest phases of MIS 5 or the Holocene. Winter temperatures declined from MIS 4 to MIS 2. Changes in temperature seasonality within MIS 5 and MIS 1 are consistent with insolation seasonality changes. Conditions became progressively more humid during MIS 5, and MIS 4 was also humid although MIS 3 was more arid. Changes in MI and GDD0 are anti-correlated, with increased MI during summer warming intervals. Comparison with other records shows glacial-interglacial changes were not unform across the circum-Mediterranean region but available quantitative reconstructions are insufficient to determine if east-west differences reflect the circulation-driven precipitation dipole seen in recent decades.
Collalti A, Ibrom A, Stockmarr A, et al., 2020, Forest production efficiency increases with growth temperature, Nature Communications, Vol: 11, ISSN: 2041-1723
Forest production efficiency (FPE) metric describes how efficiently the assimilated carbon is partitioned into plants organs (biomass production, BP) or—more generally—for the production of organic matter (net primary production, NPP). We present a global analysis of the relationship of FPE to stand-age and climate, based on a large compilation of data on gross primary production and either BP or NPP. FPE is important for both forest production and atmospheric carbon dioxide uptake. We find that FPE increases with absolute latitude, precipitation and (all else equal) with temperature. Earlier findings—FPE declining with age—are also supported by this analysis. However, the temperature effect is opposite to what would be expected based on the short-term physiological response of respiration rates to temperature, implying a top-down regulation of carbon loss, perhaps reflecting the higher carbon costs of nutrient acquisition in colder climates. Current ecosystem models do not reproduce this phenomenon. They consistently predict lower FPE in warmer climates, and are therefore likely to overestimate carbon losses in a warming climate.
Paillassa J, Wright I, Prentice IC, et al., 2020, When and where soil is important to modify the carbon and water economy of leaves., New Phytologist, Vol: 228, Pages: 121-135, ISSN: 0028-646X
Photosynthetic “least‐cost” theory posits that the optimal trait combination for a given environment is that where the summed costs of photosynthetic water and nutrient acquisition/use are minimised. The effects of soil water and nutrient availability on photosynthesis should be stronger as climate‐related costs for both resources increase.Two independent datasets of photosynthetic traits, Globamax (1509 species, 288 sites) and Glob13C (3645 species, 594 sites), were used to quantify biophysical and biochemical limitations of photosynthesis and the key variable Ci/Ca (CO2 drawdown during photosynthesis). Climate and soil variables were associated with both datasets.The biochemical photosynthetic capacity was higher on alkaline soils. This effect was strongest at more arid sites, where water unit‐costs are presumably higher. Higher values of soil silt and depth increased Ci/Ca, likely by providing greater H2O supply, alleviating biophysical photosynthetic limitation when soil water is scarce.Climate is important in controlling the optimal balance of H2O and N costs for photosynthesis, but soil properties change these costs, both directly and indirectly. In total, soil properties modify the climate‐demand driven predictions of Ci/Ca by up to 30% at a global scale.
Lavergne A, Sandoval D, Hare VJ, et al., 2020, Impacts of soil water stress on the acclimated stomatal limitation of photosynthesis: insights from stable carbon isotope data., Global Change Biology, Vol: 26, Pages: 7158-7172, ISSN: 1354-1013
Atmospheric aridity and drought both influence physiological function in plant leaves, but their relative contributions to changes in the ratio of leaf-internal to ambient partial pressure of CO2 (χ) - an index of adjustments in both stomatal conductance and photosynthetic rate to environmental conditions - are difficult to disentangle. Many stomatal models predicting χ include the influence of only one of these drivers. In particular, the least-cost optimality hypothesis considers the effect of atmospheric demand for water on χ but does not predict how soils with reduced water further influence χ, potentially leading to an overestimation of χ under dry conditions. Here we use a large network of stable carbon isotope measurements in C3 woody plants to examine the acclimated response of χ to soil water stress. We estimate the ratio of cost factors for carboxylation and transpiration (β) expected from the theory to explain the variance in the data, and investigate the responses of β (and thus χ) to soil water content and suction across seed plant groups, leaf phenological types and regions. Overall, β decreases linearly with soil drying, implying that the cost of water transport along the soil-plant-atmosphere continuum increases as water available in the soil decreases. However, despite contrasting hydraulic strategies, the stomatal responses of angiosperms and gymnosperms to soil water tend to converge, consistent with the optimality theory. The prediction of β as a simple, empirical function of soil water significantly improves χ predictions by up to 6.3 ± 2.3% (mean ± sd of adjusted-R2 ) over 1980-2018 and results in a reduction of around 2% of mean χ values across the globe. Our results highlight the importance of soil water status on stomatal functions and plant water-use efficiency, and suggest the implementation of trait-based hydraulic functions into the model to account for soil water stre
Hantson S, Kelley DI, Arneth A, et al., 2020, Quantitative assessment of fire and vegetation properties in simulations with fire-enabled vegetation models from the Fire Model Intercomparison Project, Geoscientific Model Development, Vol: 13, Pages: 3299-3318, ISSN: 1991-959X
Global fire-vegetation models are widely used to assess impacts of environmental change on fire regimes and the carbon cycle and to infer relationships between climate, land use and fire. However, differences in model structure and parameterizations, in both the vegetation and fire components of these models, could influence overall model performance, and to date there has been limited evaluation of how well different models represent various aspects of fire regimes. The Fire Model Intercomparison Project (FireMIP) is coordinating the evaluation of state-of-the-art global fire models, in order to improve projections of fire characteristics and fire impacts on ecosystems and human societies in the context of global environmental change. Here we perform a systematic evaluation of historical simulations made by nine FireMIP models to quantify their ability to reproduce a range of fire and vegetation benchmarks. The FireMIP models simulate a wide range in global annual total burnt area (39–536 Mha) and global annual fire carbon emission (0.91–4.75 Pg C yr−1) for modern conditions (2002–2012), but most of the range in burnt area is within observational uncertainty (345–468 Mha). Benchmarking scores indicate that seven out of nine FireMIP models are able to represent the spatial pattern in burnt area. The models also reproduce the seasonality in burnt area reasonably well but struggle to simulate fire season length and are largely unable to represent interannual variations in burnt area. However, models that represent cropland fires see improved simulation of fire seasonality in the Northern Hemisphere. The three FireMIP models which explicitly simulate individual fires are able to reproduce the spatial pattern in number of fires, but fire sizes are too small in key regions, and this results in an underestimation of burnt area. The correct representation of spatial and seasonal patterns in vegetation appears
Joos F, Spahni R, Stocker BD, et al., 2020, N2O changes from the Last Glacial Maximum to the preindustrial - Part 2: terrestrial N2O emissions and carbon-nitrogen cycle interactions, BIOGEOSCIENCES, Vol: 17, Pages: 3511-3543, ISSN: 1726-4170
Waring B, Neumann M, Prentice IC, et al., 2020, What role can forests play in tackling climate change?, What role can forests play in tackling climate change?, www.imperial.ac.uk/grantham, Publisher: Grantham Institute, Discussion paper 6
This discussion paper consolidates knowledge on the potential environmental, economic and societal benefits of using trees to reduce the concentration of carbon dioxide in the atmosphere. It highlights areas for further research and defines the limits of trees’ ability to halt the progress of climate change.
Qiao S, Wang H, Harrison SP, et al., 2020, Extending a first-principles primary production model to predict wheat yields, Agricultural and Forest Meteorology, Vol: 287, Pages: 1-16, ISSN: 0168-1923
Climate exerts a major influence on crop development and yield. Despite extensive modelling efforts, there is still considerable uncertainty about the consequences of a changing climate for the yields of major crops. Existing crop models are complex and rely on many assumptions and parameters, motivating a quest for more parsimonious models with stronger theoretical and empirical foundations. This paper presents a prototype of such a model for wheat, informed by measurements of gross primary production (GPP), biomass and yield at research sites across the wheat-growing regions of China. First, GPP was predicted using a recently developed first-principles model driven only by climate, carbon dioxide (CO2) concentration, and light absorbed by leaves. Modelled GPP was shown to agree well with eddy-covariance measurements. Second, the data were used to show that above-ground biomass (AB) is proportional to time-integrated GPP, and that grain yield shows a saturating relationship with AB. Simple empirical equations based on these findings were combined with modelled GPP to predict yield, including propagated errors due to parameter uncertainty in both the GPP model and the empirical equations. The resulting 'hybrid' model, applied in a variety of climates, successfully predicted measured interannual variations in AB and yield. Third, the model was extended to include a phenology scheme, a mass-balance equation relating mean leaf area index to accumulated GPP over the growth phase, and an independently observed response of leaf mass-per-area to CO2. Sensitivity analyses and scenario runs with this extended model showed a positive but saturating (at ∼600 ppm) response of yield to rising CO2, consistent with experimental evidence. This positive effect was partially counteracted by a net negative response of yield to increasing temperature, caused by increasing photorespiration and an accelerated growth cycle.
Peng Y, Bloomfield K, Prentice IC, 2020, A theory of plant function helps to explain leaf-trait and productivity responses to elevation, New Phytologist, Vol: 226, Pages: 1274-1284, ISSN: 0028-646X
Several publications have examined leaf‐trait and carbon‐cycling shifts along an Amazon‐Andes transect spanning 3.5 km elevation and 16℃ mean annual temperature. Photosynthetic capacity was previously shown to increase as temperature declines with increasing elevation, counteracting enzyme‐kinetic effects. Primary production declines, nonetheless, due to decreasing light availability. We aimed to predict leaf‐trait and production gradients from first principles, using published data to test an emerging theory whereby photosynthetic traits and primary production depend on optimal acclimation and/or adaptation to environment.We re‐analysed published data for 210 species at 25 sites, fitting linear relationships to elevation for both predicted and observed photosynthetic traits and primary production.Declining leaf‐internal/ambient CO2 ratio (χ) and increasing carboxylation (Vcmax) and electron‐transport (Jmax) capacities with increasing elevation were predicted. Increases in leaf nitrogen content with elevation were explained by increasing Vcmax and leaf mass‐per‐area. Leaf and soil phosphorus covaried, but after controlling for elevation, no nutrient metric accounted for any additional variance in photosynthetic traits. Primary production was predicted to decline with elevation.This analysis unifies leaf and ecosystem observations in a common theoretical framework. The insensitivity of primary production to temperature is shown to emerge as a consequence of the optimization of photosynthetic traits.
Terrer C, Jackson RB, Prentice IC, et al., 2020, Nitrogen and phosphorus constrain the CO2 fertilization of global plant biomass (vol 21, pg 561, 2020), NATURE CLIMATE CHANGE, Vol: 10, Pages: 696-697, ISSN: 1758-678X
Plants and vegetation play a critical—but largely unpredictable—role in global environmental changes due to the multitude of contributing processes at widely different spatial and temporal scales. In this Perspective, we explore approaches to master this complexity and improve our ability to predict vegetation dynamics by explicitly taking account of principles that constrain plant and ecosystem behaviour: natural selection, self-organization and entropy maximization. These ideas are increasingly being used in vegetation models, but we argue that their full potential has yet to be realized. We demonstrate the power of natural selection-based optimality principles to predict photosynthetic and carbon allocation responses to multiple environmental drivers, as well as how individual plasticity leads to the predictable self-organization of forest canopies. We show how models of natural selection acting on a few key traits can generate realistic plant communities and how entropy maximization can identify the most probable outcomes of community dynamics in space- and time-varying environments. Finally, we present a roadmap indicating how these principles could be combined in a new generation of models with stronger theoretical foundations and an improved capacity to predict complex vegetation responses to environmental change.
Waring B, Neumann M, Prentice IC, et al., 2020, Forests and decarbonization: roles of natural and planted forests, Frontiers in Forests and Global Change, Vol: 3, ISSN: 2624-893X
The severe consequences of human disruptions to the global carbon cycle have prompted intense interest in strategies to reduce atmospheric CO2 concentrations. Because growing forests capture CO2 in their biomass and soils, large-scale tree planting efforts have been advertised as a viable way to counteract anthropogenic emissions as part of net-zero emission strategies. Here, we assess the potential impact of reforestation and afforestation on the global climate system, and identify ecological, economic, and societal implications of such efforts.
Wei D, Prentice IC, Harrison SP, 2020, The climatic space of European pollen taxa, ECOLOGY, Vol: 101, ISSN: 0012-9658
Dong N, Prentice IC, Wright IJ, et al., 2020, Components of leaf-trait variation along environmental gradients, NEW PHYTOLOGIST, Vol: 228, Pages: 82-94, ISSN: 0028-646X
Cleator SF, Harrison SP, Nichols NK, et al., 2020, A new multivariable benchmark for Last Glacial Maximum climate simulations, CLIMATE OF THE PAST, Vol: 16, Pages: 699-712, ISSN: 1814-9324
Wang H, Atkin OK, Keenan TF, et al., 2020, Acclimation of leaf respiration consistent with optimal photosynthetic capacity, Global Change Biology, Vol: 26, Pages: 2573-2583, ISSN: 1354-1013
Plant respiration is an important contributor to the proposed positive global carbon-cycle feedback to climate change. However, as a major component, leaf mitochondrial (‘dark’) respiration (Rd) differs among species adapted to contrasting environments and is known to acclimate to sustained changes in temperature. No accepted theory explains these phenomena or predicts its magnitude. Here we propose that the acclimation of Rd follows an optimal behaviour related to the need to maintain long-term average photosynthetic capacity (Vcmax) so that available environmental resources can be most efficiently used for photosynthesis. To test this hypothesis, we extend photosynthetic co-ordination theory to predict the acclimation of Rd to growth temperaturevia a link to Vcmax, and compare predictions to a global set of measurements from 112 sites spanning all terrestrial biomes. This extended co-ordination theory predicts that field-measured Rd should increase by 3.7% and Vcmax by 5.5% per degree increase in growth temperature. These acclimated responses to 50growth temperature are less steep than the corresponding instantaneous responses, which increase 8.1% and 9.9% per degree of measurement temperature for Rd and Vcmax, respectively. Data-fitted regression slopes proof indistinguishable from the values predicted by our theory, and smaller than the instantaneous slopes. Theory and data are also shown to agree that the basal rates ofboth Rd and Vcmax assessed at 25 ̊C decline by ~ 4.4% per degree increase in growth temperature. These results provide a parsimonious general theory for Rd acclimation to temperature that is simpler – and potentially more reliable – than the plant functional type-based leaf respiration schemes currently employed in most ecosystem and land-surface models.
Wang H, Smith NG, Harrison SP, et al., 2020, P-model v1.0: An optimality-based light use efficiency model forsimulating ecosystem gross primary production, Geoscientific Model Development, ISSN: 1991-959X
Cleator SF, Harrison SP, Nichols NK, et al., 2020, RVAR
Make spatially coherent gridded maps of the palaeoclimate by combining site-based pollen reconstructions and climate model outputs using a conditioned 3D variational data assimilation method.
Lavergne A, Voelker S, Csank A, et al., 2020, Historical changes in the stomatal limitation of photosynthesis: empirical support for an optimality principle, New Phytologist, Vol: 225, Pages: 2484-2497, ISSN: 0028-646X
The ratio of leaf‐internal (ci) to ambient (ca) partial pressure of CO2, defined here as χ, is an index of adjustments in both leaf stomatal conductance and photosynthetic rate to environmental conditions. Measurements and proxies of this ratio can be used to constrain vegetation models uncertainties for predicting terrestrial carbon uptake and water use.We test a theory based on the least‐cost optimality hypothesis for modelling historical changes in χ over the 1951‐2014 period, across different tree species and environmental conditions, as reconstructed from stable carbon isotopic measurements across a global network of 103 absolutely‐dated tree‐ring chronologies. The theory predicts optimal χ as a function of air temperature, vapour pressure deficit, ca and atmospheric pressure.The theoretical model predicts 39% of the variance in χ values across sites and years, but underestimates the inter‐site variability in the reconstructed χ trends, resulting in only 8% of the variance in χ trends across years explained by the model.Overall, our results support theoretical predictions that variations in χ are tightly regulated by the four environmental drivers. They also suggest that explicitly accounting for the effects of plant‐available soil water and other site‐specific characteristics might improve the predictions.
Collalti A, Tjoelker MG, Hoch G, et al., 2020, Plant respiration: controlled by photosynthesis or biomass?, Global Change Biology, Vol: 26, Pages: 1739-1753, ISSN: 1354-1013
Two simplifying hypotheses have been proposed for whole‐plant respiration. One links respiration to photosynthesis; the other to biomass. Using a first‐principles carbon balance model with a prescribed live woody biomass turnover, applied at a forest research site where multidecadal measurements are available for comparison, we show that if turnover is fast the accumulation of respiring biomass is low and respiration depends primarily on photosynthesis; while if turnover is slow the accumulation of respiring biomass is high and respiration depends primarily on biomass. But the first scenario is inconsistent with evidence for substantial carry‐over of fixed carbon between years, while the second implies far too great an increase in respiration during stand development—leading to depleted carbohydrate reserves and an unrealistically high mortality risk. These two mutually incompatible hypotheses are thus both incorrect. Respiration is not linearly related either to photosynthesis or to biomass, but it is more strongly controlled by recent photosynthates (and reserve availability) than by total biomass.
Stocker BD, Wang H, Smith NG, et al., 2020, P-model v1.0: an optimality-based light use efficiency model forsimulating ecosystem gross primary production, Geoscientific Model Development, ISSN: 1991-959X
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 diverge 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).
Kattge J, Bönisch G, Díaz S, et al., 2020, TRY plant trait database - enhanced coverage and open access, Global Change Biology, Vol: 26, Pages: 119-188, ISSN: 1354-1013
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 ecosystems 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 biodiversity 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. For example, we have achieved almost nearly complete global coverage of ‘plant growth form’. However, most traits relevant for ecology and vegetation modelling are characterized by intraspecific variation and trait-environmental relationships; therefore, for many purposes, these traits have to be measured on individual plants in their respective environment. Despite unprecedented data coverage, we observe a humbling lack of completeness and representativeness of these traits in many aspects. We, therefore, conclude that reducing data gaps and biases in the TRY database requires a coordinated approach to data mobilization and in-situ trait measurements. This can only be achieved in collaboration with other initiatives.
Halbritter AH, De Boeck HJ, Eycott AE, et al., 2020, The handbook for standardised field and laboratory measurements in terrestrial climate-change experiments and observational studies, Methods in Ecology and Evolution, Vol: 11, Pages: 22-37, ISSN: 2041-210X
Climate change is a worldwide threat to biodiversity and ecosystem structure, functioning, and services. To understand the underlying drivers and mechanisms, and to predict the consequences for nature and people, we urgently need better understanding of the direction and magnitude of climate‐change impacts across the soil–plant–atmosphere continuum. An increasing number of climate‐change studies is creating new opportunities for meaningful and high‐quality generalisations and improved process understanding. However, significant challenges exist related to data availability and/or compatibility across studies, compromising opportunities for data re‐use, synthesis, and upscaling. Many of these challenges relate to a lack of an established “best practice” for measuring key impacts and responses. This restrains our current understanding of complex processes and mechanisms in terrestrial ecosystems related to climate change.
Lewis SL, Mitchard ETA, Prentice C, et al., 2019, Comment on "The global tree restoration potential", Science, Vol: 366, ISSN: 0036-8075
Bastin et al (Reports, 5 July 2019, p. 76) state that the restoration potential of new forests globally is 205 gigatonnes of carbon, conclude that "global tree restoration is our most effective climate change solution to date," and state that climate change will drive the loss of 450 million hectares of existing tropical forest by 2050. Here we show that these three statements are incorrect.
Cleator SF, Harrison SP, Nichols NK, et al., 2019, A method for generating coherent spatially explicit maps of seasonal palaeoclimates from site-based reconstructions, Journal of Advances in Modeling Earth Systems, ISSN: 1942-2466
We describe a method for reconstructing spatially explicit maps of seasonal palaeoclimate variables from site‐based reconstructions. Using a 3D‐Variational technique, the method finds the best statistically unbiased, and spatially continuous, estimate of the palaeoclimate anomalies through combining the site‐based reconstructions and a prior estimate of the palaeoclimate state. By assuming a set of correlations in the error of the prior, the resulting climate is smoothed both from month to month and from grid cell to grid cell. The amount of smoothing can be controlled through the choice of two length‐scale values. The method is applied to a set of reconstructions of the climate of the Last Glacial Maximum (ca. 21,000 years ago, yr BP) for southern Europe derived from pollen data with a prior derived from results from the third phase of the Palaeoclimate Intercomparison Project (PMIP3). We demonstrate how to choose suitable values for the smoothing length scales from the datasets used in the reconstruction.
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