Publications
433 results found
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
Leaf area (LA), mass per area (LMA), nitrogen per unit area (Narea) and the leaf-internal to ambient CO2 ratio (χ) are fundamental traits for plant functional ecology and vegetation modelling. Here we aimed to assess how their variation, within and between species, tracks environmental gradients.Measurements were made on 705 species from 116 sites within a broad north–south transect from tropical to temperate Australia. Trait responses to environment were quantified using multiple regression; within- and between-species responses were compared using analysis of covariance and trait-gradient analysis.Leaf area, the leaf economics spectrum (indexed by LMA and Narea) and χ (from stable carbon isotope ratios) varied almost independently among species. Across sites, however, χ and LA increased with mean growing-season temperature (mGDD0) and decreased with vapour pressure deficit (mVPD0) and soil pH. LMA and Narea showed the reverse pattern. Climate responses agreed with expectations based on optimality principles. Within-species variability contributed < 10% to geographical variation in LA but > 90% for χ, with LMA and Narea intermediate.These findings support the hypothesis that acclimation within individuals, adaptation within species and selection among species combine to create predictable relationships between traits and environment. However, the contribution of acclimation/adaptation vs species selection differs among traits.
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
We present a new global reconstruction of seasonal climates at the Last Glacial Maximum (LGM, 21 000 years BP) made using 3-D variational data assimilation with pollen-based site reconstructions of six climate variables and the ensemble average of the PMIP3—CMIP5 simulations as a prior (initial estimate of LGM climate). We assume that the correlation matrix of the uncertainties in the prior is both spatially and temporally Gaussian, in order to produce a climate reconstruction that is smoothed both from month to month and from grid cell to grid cell. The pollen-based reconstructions include mean annual temperature (MAT), mean temperature of the coldest month (MTCO), mean temperature of the warmest month (MTWA), growing season warmth as measured by growing degree days above a baseline of 5 ∘C (GDD5), mean annual precipitation (MAP), and a moisture index (MI), which is the ratio of MAP to mean annual potential evapotranspiration. Different variables are reconstructed at different sites, but our approach both preserves seasonal relationships and allows a more complete set of seasonal climate variables to be derived at each location. We further account for the ecophysiological effects of low atmospheric carbon dioxide concentration on vegetation in making reconstructions of MAP and MI. This adjustment results in the reconstruction of wetter climates than might otherwise be inferred from the vegetation composition. Finally, by comparing the uncertainty contribution to the final reconstruction, we provide confidence intervals on these reconstructions and delimit geographical regions for which the palaeodata provide no information to constrain the climate reconstructions. The new reconstructions will provide a benchmark created using clear and defined mathematical procedures that can be used for evaluation of the PMIP4–CMIP6 entry-card LGM simulations and are available at https://doi.org/10.17864/1947.244 (Cleator et al., 2020b).
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.
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, Vol: 13, Pages: 1545-1571, 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).
Lavergne A, Graven H, Prentice IC, 2020, Disentangling the relative contributions of atmospheric demand for water and soil water availability on the stomatal limitation of photosynthesis
<jats:p> &lt;p&gt;Plants open and close their stomata in response to changes in the environment, so they can absorb the CO&lt;sub&gt;2&lt;/sub&gt; they need to grow, while also avoid drying out. Since the activities of leaf stomata determine the exchanges of carbon and water between the vegetation and the atmosphere, it is crucial to incorporate their responses to environmental pressure into the vegetation models predicting carbon and water fluxes on broad spatial and temporal scales. The least-cost optimality theory proposes a simple way to predict leaf behaviour, in particular changes in the ratio of leaf internal (&lt;em&gt;c&lt;/em&gt;&lt;sub&gt;i&lt;/sub&gt;) to ambient (&lt;em&gt;c&lt;/em&gt;&lt;sub&gt;a&lt;/sub&gt;) partial pressure of CO&lt;sub&gt;2&lt;/sub&gt;, from four environmental variables, i.e. &lt;em&gt;c&lt;/em&gt;&lt;sub&gt;a&lt;/sub&gt;, growing-season temperature (&lt;em&gt;T&lt;/em&gt;&lt;sub&gt;g&lt;/sub&gt;), atmospheric vapour pressure deficit (&lt;em&gt;D&lt;/em&gt;&lt;sub&gt;g&lt;/sub&gt;), and atmospheric pressure (as indexed by elevation, &lt;em&gt;z&lt;/em&gt;). However, even though the theory considers the effect of atmospheric demand for water on &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;, it does not predict how dry soils with reduced soil water availability further influence &lt;em&gt;c&lt;/em&
Mengoli G, Prentice IC, Harrison SP, 2020, Adapting an optimality-based model to predict half-hourly carbon uptake by ecosystems
<jats:p> &lt;p&gt;Carbon dioxide (CO&lt;sub&gt;2&lt;/sub&gt;) uptake by leaves and its conversion into sugar by photosynthesis &amp;#8211; gross primary production (GPP) &amp;#8211; is the basis for vegetation growth. GPP is important for the carbon cycle, and its interactions with climate are a subject of study in Earth System modelling. One assumption of many current ecosystem models is that key photosynthetic traits, such as the capacities for carboxylation (V&lt;sub&gt;cmax&lt;/sub&gt;) and electron transport (J&lt;sub&gt;max&lt;/sub&gt;&amp;#173;) for ribulose-1,5-bisphosphate (RuBP) regeneration, are constant in time for any given plant functional type. Optimality theory predicts they should vary systematically with growth conditions, both in space and in time, and are not necessarily depend on the plant functional type. Moreover, theory makes specific, quantitative predictions about their (acclimated) community-mean values, predictions well supported by evidence. Neglecting such acclimation could lead to incorrect model estimates of the responses of primary production to climate change.&lt;/p&gt;&lt;p&gt;We focus on a proof-of-concept based on a primary production model, the P-model &amp;#8211; which combines the Farquhar-von Caemmerer-Berry model for C&lt;sub&gt;3&lt;/sub&gt; photosynthesis with eco-evolutionary optimality principles for the co-optimization of carboxylation and water transport costs &amp;#8211; to allow the model to reproduce short-term variations in photosynthesis and transpiration as well as longer-term, acclimated variations. Key to this effort is explicitly separating the instantaneous responses of photosynthetic rates, and the slower acclimation of photosynthetic traits. The model also includes a dynamic optimiz
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.
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.
Cleator SF, Harrison SP, Nichols NK, et al., 2019, A method for generating coherent spatially explicit maps of seasonal palaeoclimates from site-based reconstructions, Publisher: arXiv
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.
Terrer C, Prentice I, Jackson R, et al., 2019, Nitrogen and phosphorus constrain the CO2 fertilization of global plant biomas, Nature Climate Change, Vol: 9, Pages: 684-689, ISSN: 1758-678X
Elevated CO2 (eCO2) experiments provide critical information to quantify the effects of rising CO2 on vegetation. Many eCO2 experiments suggest that nutrient limitations modulate the local magnitude of the eCO2 effect on plant biomass, but the global extent of these limitations has not been empirically quantified, complicating projections of the capacity of plants to take up CO2. Here, we present the first data-driven global quantification of the eCO2 effect on biomass based on 138 eCO2 experiments. The strength of CO2 fertilization is primarily driven by nitrogen (N) in ~65% of global vegetation, and by phosphorus (P) in ~25% of global vegetation, with N- or P-limitation modulated by mycorrhizal association. Our approach suggests that CO2 levels expected by 2100 can potentially enhance plant biomass by 12±3% above current values, equivalent to 59±13 PgC. The global-scale response to eCO2 we derive from experiments is similar to past changes in greenness9 and biomass10 with rising CO2, suggesting that CO2 will continue to stimulate plant biomass in the future despite the constraining effect of soil nutrients. Our research reconciles conflicting evidence on CO2 fertilization across scales and provides an empirical estimate of the biomass sensitivity to eCO2 that may help to constrain climate projections.
Collalti A, Prentice I, 2019, Is NPP proportional to GPP? Waring’s hypothesis twenty years on, Tree Physiology, Vol: 39, Pages: 1473-1483, ISSN: 1758-4469
Gross primary production (GPP) is partitioned to autotrophic respiration (Ra) and net primary production (NPP), the latter being used to build plant tissues and synthesize non-structural and secondary compounds. Waring et al. (1998) suggested that a NPP:GPP ratio of 0.47 ± 0.04 (s.d.) is universal across biomes, tree species and stand ages. Representing NPP in models as a fixed fraction of GPP, they argued, would be both simpler and more accurate than trying to simulate Ra mechanistically. This paper reviews progress in understanding the NPP:GPP ratio in forests during the 20 years since Waring et al.. Research has confirmed the existence of pervasive acclimation mechanisms that tend to stabilize the NPP:GPP ratio, and indicates that Ra should not be modelled independently of GPP. Nonetheless, studies indicate that the value of this ratio is influenced by environmental factors, stand age and management. The average NPP:GPP ratio in over 200 studies, representing different biomes, species and forest stand ages, was found to be 0.46, consistent with the central value that Waring et al. proposed but with a much larger standard deviation (± 0.12) and a total range (0.22 to 0.79) that is too large to be disregarded.
Lavergne A, Graven H, De Kauwe MG, et al., 2019, Observed and modelled historical trends in the water use efficiency of plants and ecosystems, Global Change Biology, Vol: 25, Pages: 2242-2257, ISSN: 1354-1013
Plant water‐use efficiency (WUE, the carbon gained through photosynthesis per unit of water lost through transpiration) is a tracer of the plant physiological controls on the exchange of water and carbon dioxide between terrestrial ecosystems and the atmosphere. At the leaf level, rising CO2 concentrations tend to increase carbon uptake (in the absence of other limitations) and to reduce stomatal conductance, both effects leading to an increase in leaf WUE. At the ecosystem level, indirect effects (e.g. increased leaf area index, soil water savings) may amplify or dampen the direct effect of CO2. Thus, the extent to which changes in leaf WUE translate to changes at the ecosystem scale remains unclear. The differences in the magnitude of increase in leaf versus ecosystem WUE as reported by several studies are much larger than would be expected with current understanding of tree physiology and scaling, indicating unresolved issues. Moreover, current vegetation models produce inconsistent and often unrealistic magnitudes and patterns of variability in leaf and ecosystem WUE, calling for a better assessment of the underlying approaches. Here, we review the causes of variations in observed and modelled historical trends in WUE over the continuum of scales from leaf to ecosystem, including methodological issues, with the aim of elucidating the reasons for discrepancies observed within and across spatial scales. We emphasize that even though physiological responses to changing environmental drivers should be interpreted differently depending on the observational scale, there are large uncertainties in each data set which are often underestimated. Assumptions made by the vegetation models about the main processes influencing WUE strongly impact the modelled historical trends. We provide recommendations for improving long‐term observation‐based estimates of WUE that will better inform the representation of WUE in vegetation models.
Joos F, Spahni R, Stocker BD, et al., 2019, N&lt;sub&gt;2&lt;/sub&gt;O changes from the Last Glacial Maximum to the preindustrial – Part II: Terrestrial N&lt;sub&gt;2&lt;/sub&gt;O emissions constrain carbon-nitrogen interactions
<jats:p>Abstract. Land ecosystems currently take up a quarter of the human-caused carbon dioxide emissions. Future projections of this carbon sink are strikingly divergent, leading to major uncertainties in projected global warming. This situation partly reflects our insufficient understanding of carbon-nitrogen (C-N) interactions and particularly of the controls on biological N fixation (BNF). It is difficult to infer ecosystem responses for century time scales, relevant for global warming, from the comparatively short instrumental records and laboratory or field experiments. Here we analyse terrestrial emissions of nitrous oxide (N2O) over the past 21,000 years as reconstructed from ice-core isotopic data and presented in part I of this study. Changing N2O emissions are interpreted to reflect changes in ecosystem N loss, plant available N, and BNF. The ice-core data reveal a 40 % increase in N2O emissions over the deglaciation, suggestive of a highly dynamic global N cycle whereby sources of plant-available N adjust to meet plant N demand and loss fluxes. Remarkably, the increase occurred in two steps, each realized within maximum two centuries, at the onsets of the northern hemisphere warming events around 14,600 and 11,700 years ago. We applied the LPX-Bern dynamic global vegetation model in deglacial simulations forced with Earth System Model climate data to investigate N2O emission patterns, mechanisms, and C-N coupling. The reconstructed increase in terrestrial emissions is broadly reproduced by the model, given the assumption that BNF positively responds to increasing N demand by plants. In contrast, assuming time- and demand-independent levels of BNF in the model to mimic progressive N limitation of plant growth results in N2O emissions that are incompatible with the reconstruction. Our results suggest the existence of (a) strong biological controls on ecosystem N acquisition, and (b) flexibility in the coupling of the C and N cycles during per
Prentice I, Stocker B, Zscheischler J, et al., 2019, Drought impacts on terrestrial primary production underestimated by satellite monitoring, Nature Geoscience, Vol: 12, Pages: 264-270, ISSN: 1752-0894
Satellite retrievals of information about the Earth’s surface are widely used to monitor global terrestrial photosynthesis and primary production and to examine the ecological impacts of droughts. Methods for estimating photosynthesis from space commonly combine information on vegetation greenness, incoming radiation, temperature, and atmospheric demand for water (vapour-pressure deficit), but do not account for the direct effects of low soil moisture. They instead rely on vapour-pressure deficit as a proxy for dryness, despite widespread evidence that soil moisture deficits have a direct impact on vegetation, independent of vapour-pressure deficit. Here, we use a globally distributed measurement network to assess the effect of soil moisture on photosynthesis, and identify a common bias in an ensemble of satellite-based estimates of photosynthesis that is governed by the magnitude of soil moisture effects on photosynthetic light-use efficiency. We develop methods to account for the influence of soil moisture and estimate that soil moisture effects reduce global annual photosynthesis by ~15%, increase interannual variability by more than 100% across 25% of the global vegetated land surface, and amplify the impacts of extreme events on primary production. These results demonstrate the importance of soil moisture effects for monitoring carbon-cycle variability and drought impacts on vegetation productivity from space.
Smith N, Keenan T, Prentice I, et al., 2019, Global photosynthetic capacity is optimized to the environment, Ecology Letters, Vol: 22, Pages: 506-517, ISSN: 1461-023X
Earth system models (ESMs) use photosynthetic capacity, indexed by the maximum Rubisco carboxylation rate (Vcmax), to simulate carbon assimilation and typically rely on empirical estimates, including an assumed dependence on leaf nitrogen determined from soil fertility. In contrast, new theory, based on biochemical coordination and co‐optimization of carboxylation and water costs for photosynthesis, suggests that optimal Vcmax can be predicted from climate alone, irrespective of soil fertility. Here, we develop this theory and find it captures 64% of observed variability in a global, field‐measured Vcmax dataset for C3 plants. Soil fertility indices explained substantially less variation (32%). These results indicate that environmentally regulated biophysical constraints and light availability are the first‐order drivers of global photosynthetic capacity. Through acclimation and adaptation, plants efficiently utilize resources at the leaf level, thus maximizing potential resource use for growth and reproduction. Our theory offers a robust strategy for dynamically predicting photosynthetic capacity in ESMs.
Bloomfield KJ, Prentice IC, Cernusak LA, et al., 2019, The validity of optimal leaf traits modelled on environmental conditions, New Phytologist, Vol: 221, Pages: 1409-1423, ISSN: 0028-646X
The ratio of leaf intercellular to ambient CO2 (χ) is modulated by stomatal conductance (gs ). These quantities link carbon (C) assimilation with transpiration, and along with photosynthetic capacities (Vcmax and Jmax ) are required to model terrestrial C uptake. We use optimisation criteria based on the growth environment to generate predicted values of photosynthetic and water-use efficiency traits and test these against a unique dataset. Leaf gas-exchange parameters and carbon isotope discrimination were analysed in relation to local climate across a continental network of study sites. Sun-exposed leaves of 50 species at seven sites were measured in contrasting seasons. Values of χ predicted from growth temperature and vapour pressure deficit were closely correlated to ratios derived from C isotope (δ13 C) measurements. Correlations were stronger in the growing season. Predicted values of photosynthetic traits, including carboxylation capacity (Vcmax ), derived from δ13 C, growth temperature and solar radiation, showed meaningful agreement with inferred values derived from gas-exchange measurements. Between-site differences in water use efficiency were, however, only weakly linked to the plant's growth environment and did not show seasonal variation. These results support the general hypothesis that many key parameters required by Earth system models are adaptive and predictable from plants' growth environments.
Zhou S-X, Prentice IC, Medlyn BE, 2019, Bridging drought experiment and modeling: Representing the differential sensitivities of leaf gas exchange to drought, Frontiers in Plant Science, Vol: 9, ISSN: 1664-462X
Global climate change is expected to increase drought duration and intensity in certain regions while increasing rainfall in others. The quantitative consequences of increased drought for ecosystems are not easy to predict. Process-based models must be informed by experiments to determine the resilience of plants and ecosystems from different climates. Here, we demonstrate what and how experimentally derived quantitative information can improve the representation of stomatal and non-stomatal photosynthetic responses to drought in large-scale vegetation models. In particular, we review literature on the answers to four key questions: (1) Which photosynthetic processes are affected under short-term drought? (2) How do the stomatal and non-stomatal responses to short-term drought vary among species originating from different hydro-climates? (3) Do plants acclimate to prolonged water stress, and do mesic and xeric species differ in their degree of acclimation? (4) Does inclusion of experimentally based plant functional type specific stomatal and non-stomatal response functions to drought help Land Surface Models to reproduce key features of ecosystem responses to drought? We highlighted the need for evaluating model representations of the fundamental eco-physiological processes under drought. Taking differential drought sensitivity of different vegetation into account is necessary for Land Surface Models to accurately model drought responses, or the drought impacts on vegetation in drier environments may be over-estimated.
Yang Y, Wang H, Harrison S, et al., 2019, Quantifying leaf trait covariation and its controls across climates and biomes, New Phytologist, Vol: 221, Pages: 155-168, ISSN: 0028-646X
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 CO2 ratio (χ), leaf economics spectrum traits (specific leaf area (SLA) versus leaf dry matter content (LDMC) and nitrogen per area (Narea)), and photosynthetic capacities (Vcmax, Jmax at 25°C). LA and χ covaried with moisture index. Site, climate, life form and family together explained 70% of trait variance. Families accounted for 17%, and climate and families together 29%. LDMC and SLA showed the largest family effects. Independent life‐form effects were small. Climate influences trait variation in part by selection for different life forms and families. Trait values derived from climate data via RDA showed substantial predictive power for trait values in the available global data sets. Systematic trait data collection across all climates and biomes is still necessary.
Prentice I, Togashi HF, Atkin OK, et al., 2018, Functional trait variation related to gap dynamics in tropical moist forests: a vegetation modelling perspective, Perspectives in Plant Ecology, Evolution and Systematics, Vol: 35, Pages: 52-64, ISSN: 1433-8319
The conventional representation of Plant Functional Types (PFTs) in Dynamic Global Vegetation Models (DGVMs) is increasingly recognized as simplistic and lacking in predictive power. Key ecophysiological traits, including photosynthetic parameters, are typically assigned single values for each PFT while the substantial trait variation within PFTs is neglected. This includes continuous variation in response to environmental factors, and differences linked to spatial and temporal niche differentiation within communities. A much stronger empirical basis is required for the treatment of continuous plant functional trait variation in DGVMs. We analyse 431 sets of measurements of leaf and plant traits, including photosynthetic measurements, on evergreen angiosperm trees in tropical moist forests of Australia and China. Confining attention to tropical moist forests, our analysis identifies trait differences that are linked to vegetation dynamic roles. Coordination theory predicts that Rubisco- and electron-transport limited rates of photosynthesis are co-limiting under field conditions. The least-cost hypothesis predicts that air-to-leaf CO2 drawdown minimizes the combined costs per unit carbon assimilation of maintaining carboxylation and transpiration capacities. Aspects of these predictions are supported for within-community trait variation linked to canopy position, just as they are for variation along spatial environmental gradients. Trait differences among plant species occupying different structural and temporal niches may provide a basis for the ecophysiological representation of vegetation dynamics in next-generation DGVMs.
Prentice IC, Thomas RT, 2018, TERRA-P: A NEW GLOBAL MONITORING SYSTEM FOR PRIMARY PRODUCTION, 38th IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Publisher: IEEE, Pages: 8734-8736, ISSN: 2153-6996
Gallego-Sala A, Charman D, Brewer S, et al., 2018, Latitudinal limits to the predicted increase of the peatland carbon sink with warming, Nature Climate Change, Vol: 8, Pages: 907-913, ISSN: 1758-678X
The carbon sink potential of peatlands depends on the balance of carbon uptake by plants and microbial decomposition. The rates of both these processes will increase with warming but it remains unclear which will dominate the global peatland response. Here we examine the global relationship between peatland carbon accumulation rates during the last millennium and planetary-scale climate space. A positive relationship is found between carbon accumulation and cumulative photosynthetically active radiation during the growing season for mid- to high-latitude peatlands in both hemispheres. However, this relationship reverses at lower latitudes, suggesting that carbon accumulation is lower under the warmest climate regimes. Projections under Representative Concentration Pathway (RCP)2.6 and RCP8.5 scenarios indicate that the present-day global sink will increase slightly until around ad 2100 but decline thereafter. Peatlands will remain a carbon sink in the future, but their response to warming switches from a negative to a positive climate feedback (decreased carbon sink with warming) at the end of the twenty-first century.
Hengl T, Walsh MG, Sanderman J, et al., 2018, Global mapping of potential natural vegetation: an assessment of machine learning algorithms for estimating land potential, PEERJ, Vol: 6, ISSN: 2167-8359
Potential natural vegetation (PNV) is the vegetation cover in equilibrium with climate, that would exist at a given location if not impacted by human activities. PNV is useful for raising public awareness about land degradation and for estimating land potential. This paper presents results of assessing machine learning algorithms—neural networks (nnet package), random forest (ranger), gradient boosting (gbm), K-nearest neighborhood (class) and Cubist—for operational mapping of PNV. Three case studies were considered: (1) global distribution of biomes based on the BIOME 6000 data set (8,057 modern pollen-based site reconstructions), (2) distribution of forest tree taxa in Europe based on detailed occurrence records (1,546,435 ground observations), and (3) global monthly fraction of absorbed photosynthetically active radiation (FAPAR) values (30,301 randomly-sampled points). A stack of 160 global maps representing biophysical conditions over land, including atmospheric, climatic, relief, and lithologic variables, were used as explanatory variables. The overall results indicate that random forest gives the overall best performance. The highest accuracy for predicting BIOME 6000 classes (20) was estimated to be between 33% (with spatial cross-validation) and 68% (simple random sub-setting), with the most important predictors being total annual precipitation, monthly temperatures, and bioclimatic layers. Predicting forest tree species (73) resulted in mapping accuracy of 25%, with the most important predictors being monthly cloud fraction, mean annual and monthly temperatures, and elevation. Regression models for FAPAR (monthly images) gave an R-square of 90% with the most important predictors being total annual precipitation, monthly cloud fraction, CHELSA bioclimatic layers, and month of the year, respectively. Further developments of PNV mapping could include using all GBIF records to map the global distribution of plant species at different taxonomic leve
Lusk CH, Clearwater MJ, Laughlin DC, et al., 2018, Frost and leaf-size gradients in forests: global patterns and experimental evidence, New Phytologist, Vol: 219, Pages: 565-573, ISSN: 0028-646X
Explanations of leaf size variation commonly focus on water availability, yet leaf size also varies with latitude and elevation in environments where water is not strongly limiting. We provide the first conclusive test of a prediction of leaf energy balance theory that may explain this pattern: large leaves are more vulnerable to night-time chilling, because their thick boundary layers impede convective exchange with the surrounding air. Seedlings of 15 New Zealand evergreens spanning 12-fold variation in leaf width were exposed to clear night skies, and leaf temperatures were measured with thermocouples. We then used a global dataset to assess several climate variables as predictors of leaf size in forest assemblages. Leaf minus air temperature was strongly correlated with leaf width, ranging from -0.9 to -3.2°C in the smallest- and largest-leaved species, respectively. Mean annual temperature and frost-free period were good predictors of evergreen angiosperm leaf size in forest assemblages, but no climate variable predicted deciduous leaf size. Although winter deciduousness makes large leaves possible in strongly seasonal climates, large-leaved evergreens are largely confined to frost-free climates because of their susceptibility to radiative cooling. Evergreen leaf size data can therefore be used to enhance vegetation models, and to infer palaeotemperatures from fossil leaf assemblages.
Fürstenau Togashi H, Prentice IC, Atkin OK, et al., 2018, Thermal acclimation of leaf photosynthetic traits in an evergreenwoodland, consistent with the co-ordination hypothesis, Biogeosciences, Vol: 15, Pages: 3461-3474, ISSN: 1726-4170
Ecosystem models commonly assume that key photosynthetic traits, such as carboxylation-capacity measured at a standard temperature, are constant in time. The temperature responses of modelled photosynthetic/respiratory rates then depend entirely on enzyme kinetics. Optimality considerations suggest this assumption may be incorrect. The ‘co-ordination hypothesis’ (that Rubisco- and electron-transport limited rates of photosynthesis are co-limiting under typical daytime conditions) predicts instead that carboxylation (<i>V<sub>cmax</i></sub>) and light-harvesting (<i>J<sub>max</i></sub>) capacities, and mitochondrial respiration in the dark (<i>R<sub>dark</i></sub>), should acclimate so that they increase with growth temperature &amp;ndash; but less steeply than their instantaneous response rates. To explore this hypothesis, photosynthetic measurements were carried out on woody species during the warm and the cool seasons in the semi-arid Great Western Woodlands, Australia, under broadly similar light environments. A consistent linear relationship between <i>V<sub>cmax</i></sub> and <i>J<sub>max</i></sub> was found across species. <i>V<sub>cmax</i></sub>, <i>J<sub>max</i></sub> and <i>R<sub>dark</i></sub> increased with temperature, but values standardized to 25&amp;thinsp;˚C declined. The <i>ci:ca</i> ratio increased slightly with temperature. The leaf <i>N:P</i> ratio was lower in the warm season. The slopes of the relationships of log-transfor
Togashi HF, Prentice IC, Atkin OK, et al., 2018, Thermal acclimation of leaf photosynthetic traits in an evergreen woodland, consistent with the coordination hypothesis, Biogeosciences, Vol: 15, Pages: 3461-3474, ISSN: 1726-4170
Ecosystem models commonly assume that key photosynthetic traits, such as carboxylation capacity measured at a standard temperature, are constant in time. The temperature responses of modelled photosynthetic or respiratory rates then depend entirely on enzyme kinetics. Optimality considerations, however, suggest this assumption may be incorrect. The "coordination hypothesis" (that Rubisco- and electron-transport-limited rates of photosynthesis are co-limiting under typical daytime conditions) predicts, instead, that carboxylation (Vcmax) capacity should acclimate so that it increases somewhat with growth temperature but less steeply than its instantaneous response, implying that Vcmax when normalized to a standard temperature (e.g. 25 °C) should decline with growth temperature. With additional assumptions, similar predictions can be made for electron-transport capacity (Jmax) and mitochondrial respiration in the dark (Rdark). To explore these hypotheses, photosynthetic measurements were carried out on woody species during the warm and the cool seasons in the semi-arid Great Western Woodlands, Australia, under broadly similar light environments. A consistent proportionality between Vcmax and Jmax was found across species. Vcmax, Jmax and Rdark increased with temperature in most species, but their values standardized to 25 °C declined. The ci : ca ratio increased slightly with temperature. The leaf N : P ratio was lower in the warm season. The slopes of the relationships between log-transformed Vcmax and Jmax and temperature were close to values predicted by the coordination hypothesis but shallower than those predicted by enzyme kinetics.
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