351 results found
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.
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.
Harrison SP, Bartlein PJ, Brovkin V, et al., 2018, The biomass burning contribution to climate-carbon-cycle feedback, Earth System Dynamics, Vol: 9, Pages: 663-677, ISSN: 2190-4979
Temperature exerts strong controls on the incidence and severity of fire. All else equal, warming is expected to increase fire-related carbon emissions, and thereby atmospheric CO2. But the magnitude of this feedback is very poorly known. We use a single-box model of the land biosphere to quantify this positive feedback from satellite-based estimates of biomass burning emissions for 2000–2014 CE and from sedimentary charcoal records for the millennium before the industrial period. We derive an estimate of the centennial-scale feedback strength of 6.5 ± 3.4 ppm CO2 per degree of land temperature increase, based on the satellite data. However, this estimate is poorly constrained, and is largely driven by the well-documented dependence of tropical deforestation and peat fires (primarily anthropogenic) on climate variability patterns linked to the El Niño–Southern Oscillation. Palaeo-data from pre-industrial times provide the opportunity to assess the fire-related climate–carbon-cycle feedback over a longer period, with less pervasive human impacts. Past biomass burning can be quantified based on variations in either the concentration and isotopic composition of methane in ice cores (with assumptions about the isotopic signatures of different methane sources) or the abundances of charcoal preserved in sediments, which reflect landscape-scale changes in burnt biomass. These two data sources are shown here to be coherent with one another. The more numerous data from sedimentary charcoal, expressed as normalized anomalies (fractional deviations from the long-term mean), are then used – together with an estimate of mean biomass burning derived from methane isotope data – to infer a feedback strength of 5.6 ± 3.2 ppm CO2 per degree of land temperature and (for a climate sensitivity of 2.8 K) a gain of 0.09 ± 0.05. This finding indicates that the positive carbon cycle feedback from increased fire provides a substantial
Bloomfield KJ, Cernusak LA, Eamus D, et al., 2018, A continental-scale assessment of variability in leaf traits: within species, across sites and between seasons., Functional Ecology, Vol: 2018, Pages: 1-15, ISSN: 0269-8463
Plant species show considerable leaf trait variability that should be accounted for in dynamic global vegetation models (DGVMs). In particular, differences in the acclimation of leaf traits during periods more and less favourable to growth have rarely been examined.We conducted a field study of leaf trait variation at seven sites spanning a range of climates and latitudes across the Australian continent; 80 native plant species were included. We measured key traits associated with leaf structure, chemistry and metabolism during the favourable and unfavourable growing seasons.Leaf traits differed widely in the degree of seasonal variation displayed. Leaf mass per unit area (Ma) showed none. At the other extreme, seasonal variation accounted for nearly a third of total variability in dark respiration (Rdark).At the non‐tropical sites, carboxylation capacity (Vcmax) at the prevailing growth temperature was typically higher in summer than in winter. When Vcmax was normalized to a common reference temperature (25°C), however, the opposite pattern was observed for about 30% of the species. This suggests that metabolic acclimation is possible, but far from universal.Intraspecific variation—combining measurements of individual plants repeated at contrasting seasons, different leaves from the same individual, and multiple conspecific plants at a given site—dominated total variation for leaf metabolic traits Vcmax and Rdark. By contrast, site location was the major source of variation (53%) for Ma. Interspecific trait variation ranged from only 13% of total variation for Vcmax up to 43% for nitrogen content per unit leaf area.These findings do not support a common practice in DGVMs of assigning fixed leaf trait values to plant functional types. Trait‐based models should allow for interspecific differences, together with spatial and temporal plasticity in leaf structural, chemical and metabolic traits.
Stocker BD, Zscheischler J, Keenan TF, et al., 2018, Quantifying soil moisture impacts on light use efficiency across biomes., New Phytologist, Vol: 218, Pages: 1430-1449, ISSN: 0028-646X
Terrestrial primary productivity and carbon cycle impacts of droughts are commonly quantified using vapour pressure deficit (VPD) data and remotely sensed greenness, without accounting for soil moisture. However, soil moisture limitation is known to strongly affect plant physiology. Here, we investigate light use efficiency, the ratio of gross primary productivity (GPP) to absorbed light. We derive its fractional reduction due to soil moisture (fLUE), separated from VPD and greenness changes, using artificial neural networks trained on eddy covariance data, multiple soil moisture datasets and remotely sensed greenness. This reveals substantial impacts of soil moisture alone that reduce GPP by up to 40% at sites located in sub-humid, semi-arid or arid regions. For sites in relatively moist climates, we find, paradoxically, a muted fLUE response to drying soil, but reduced fLUE under wet conditions. fLUE identifies substantial drought impacts that are not captured when relying solely on VPD and greenness changes and, when seasonally recurring, are missed by traditional, anomaly-based drought indices. Counter to common assumptions, fLUE reductions are largest in drought-deciduous vegetation, including grasslands. Our results highlight the necessity to account for soil moisture limitation in terrestrial primary productivity data products, especially for drought-related assessments.
Wang H, Harrison SP, Prentice IC, et al., 2017, The China Plant Trait Database: toward a comprehensive regional compilation of functional traits for land plants., Ecology, Vol: 99, Pages: 500-500, ISSN: 0012-9658
Plant functional traits provide information about adaptations to climate and environmental conditions, and can be used to explore the existence of alternative plant strategies within ecosystems. Trait data are also increasingly being used to provide parameter estimates for vegetation models. Here we present a new database of plant functional traits from China. Most global climate and vegetation types can be found in China, and thus the database is relevant for global modeling. The China Plant Trait Database contains information on morphometric, physical, chemical, and photosynthetic traits from 122 sites spanning the range from boreal to tropical, and from deserts and steppes through woodlands and forests, including montane vegetation. Data collection at each site was based either on sampling the dominant species or on a stratified sampling of each ecosystem layer. The database contains information on 1,215 unique species, though many species have been sampled at multiple sites. The original field identifications have been taxonomically standardized to the Flora of China. Similarly, derived photosynthetic traits, such as electron-transport and carboxylation capacities, were calculated using a standardized method. To facilitate trait-environment analyses, the database also contains detailed climate and vegetation information for each site. The data set is released under a Creative Commons BY license. When using the data set, we kindly request that you cite this article, recognizing the hard work that went into collecting the data and the authors' willingness to make it publicly available.
Terrer C, Vicca S, Stocker BD, et al., 2017, Ecosystem responses to elevated CO2 governed by plant-soil interactions and the cost of nitrogen acquisition., New Phytologist, Vol: 217, Pages: 507-522, ISSN: 0028-646X
Contents Summary I. II. III. IV. References SUMMARY: Land ecosystems sequester on average about a quarter of anthropogenic CO2 emissions. It has been proposed that nitrogen (N) availability will exert an increasingly limiting effect on plants' ability to store additional carbon (C) under rising CO2 , but these mechanisms are not well understood. Here, we review findings from elevated CO2 experiments using a plant economics framework, highlighting how ecosystem responses to elevated CO2 may depend on the costs and benefits of plant interactions with mycorrhizal fungi and symbiotic N-fixing microbes. We found that N-acquisition efficiency is positively correlated with leaf-level photosynthetic capacity and plant growth, and negatively with soil C storage. Plants that associate with ectomycorrhizal fungi and N-fixers may acquire N at a lower cost than plants associated with arbuscular mycorrhizal fungi. However, the additional growth in ectomycorrhizal plants is partly offset by decreases in soil C pools via priming. Collectively, our results indicate that predictive models aimed at quantifying C cycle feedbacks to global change may be improved by treating N as a resource that can be acquired by plants in exchange for energy, with different costs depending on plant interactions with microbial symbionts.
Wang H, Prentice IC, Keenan TF, et al., 2017, Towards a universal model for carbon dioxide uptake by plants, NATURE PLANTS, Vol: 3, Pages: 734-741, ISSN: 2055-026X
Leaf size varies by over a 100,000-fold among species worldwide. Although 19th-century plant geographers noted that the wet tropics harbor plants with exceptionally large leaves, the latitudinal gradient of leaf size has not been well quantified nor the key climatic drivers convincingly identified. Here, we characterize worldwide patterns in leaf size. Large-leaved species predominate in wet, hot, sunny environments; small-leaved species typify hot, sunny environments only in arid conditions; small leaves are also found in high latitudes and elevations. By modeling the balance of leaf energy inputs and outputs, we show that daytime and nighttime leaf-to-air temperature differences are key to geographic gradients in leaf size. This knowledge can enrich “next-generation” vegetation models in which leaf temperature and water use during photosynthesis play key roles.
Dong N, Prentice IC, Harrison SP, et al., 2017, Biophysical homoeostasis of leaf temperature: A neglected process for vegetation and land-surface modelling, Global Ecology and Biogeography, Vol: 26, Pages: 998-1007, ISSN: 1466-822X
AimLeaf and air temperatures are seldom equal, but many vegetation models assume that they are. Land-surface models calculate canopy temperatures, but how well they do so is unknown. We encourage consideration of the leaf- and canopy-to-air temperature difference (ΔΤ) as a benchmark for land-surface modelling and an important feature of plant and ecosystem function.LocationTropical SW China.Time period2013.Major Taxa studiesTropical trees.MethodsWe illustrate diurnal cycles of leaf- and canopy-to-air temperature difference (ΔΤ) with field measurements in a tropical dry woodland and with continuous monitoring data in a tropical seasonal forest. The Priestley–Taylor (PT) and Penman–Monteith (PM) approaches to evapotranspiration are used to provide insights into the interpretation and prediction of ΔT. Field measurements are also compared with land-surface model results obtained with the Joint U.K. Land Environment Simulator (JULES) set up for the conditions of the site.ResultsThe ΔT followed a consistent diurnal cycle, with negative values at night (attributable to negative net radiation) becoming positive in the morning, reaching a plateau and becoming negative again when air temperature exceeded a ‘crossover’ in the 24–29 °C range. Daily time courses of ΔT could be approximated by either the PT or the PM model, but JULES tended to underestimate the magnitude of negative ΔT.Main conclusionsLeaves with adequate water supply are partly buffered against air-temperature variations, through a passive biophysical mechanism. This is likely to be important for optimal leaf function, and land-surface and vegetation models should aim to reproduce it.
Keenan TF, Prentice IC, Canadell JG, et al., 2017, Corrigendum: Recent pause in the growth rate of atmospheric CO2 due to enhanced terrestrial carbon uptake, Nature Communications, Vol: 8, ISSN: 2041-1723
Colloff MJ, Lavorel S, van Kerkhoff LE, et al., 2017, Transforming conservation science and practice for a postnormal world., Conservation Biology, Vol: 31, Pages: 1008-1017, ISSN: 0888-8892
We examine issues to consider when reframing conservation science and practice in the context of global change. New framings of the links between ecosystems and society are emerging that are changing peoples' values and expectations of nature, resulting in plural perspectives on conservation. Reframing conservation for global change can thus be regarded as a stage in the evolving relationship between people and nature rather than some recent trend. New models of how conservation links with transformative adaptation include how decision contexts for conservation can be reframed and integrated with an adaptation pathways approach to create new options for global-change-ready conservation. New relationships for conservation science and governance include coproduction of knowledge that supports social learning. New processes for implementing adaptation for conservation outcomes include deliberate practices used to develop new strategies, shift world views, work with conflict, address power and intergenerational equity in decisions, and build consciousness and creativity that empower agents to act. We argue that reframing conservation for global change requires scientists and practitioners to implement approaches unconstrained by discipline and sectoral boundaries, geopolitical polarities, or technical problematization. We consider a stronger focus on inclusive creation of knowledge and the interaction of this knowledge with societal values and rules is likely to result in conservation science and practice that meets the challenges of a postnormal world.
Goll DS, Winkler AJ, Raddatz T, et al., 2017, Carbon-nitrogen interactions in idealized simulations with JSBACH (version 3.10), Geoscientific Model Development, Vol: 10, Pages: 2009-2030, ISSN: 1991-959X
Recent advances in the representation of soil carbon decomposition and carbon–nitrogen interactions implemented previously into separate versions of the land surface scheme JSBACH are here combined in a single version, which is set to be used in the upcoming 6th phase of coupled model intercomparison project (CMIP6).Here we demonstrate that the new version of JSBACH is able to reproduce the spatial variability in the reactive nitrogen-loss pathways as derived from a compilation of δ15N data (R = 0. 76, root mean square error (RMSE) = 0. 2, Taylor score = 0. 83). The inclusion of carbon–nitrogen interactions leads to a moderate reduction (−10 %) of the carbon-concentration feedback (βL) and has a negligible effect on the sensitivity of the land carbon cycle to warming (γL) compared to the same version of the model without carbon–nitrogen interactions in idealized simulations (1 % increase in atmospheric carbon dioxide per year). In line with evidence from elevated carbon dioxide manipulation experiments, pronounced nitrogen scarcity is alleviated by (1) the accumulation of nitrogen due to enhanced nitrogen inputs by biological nitrogen fixation and reduced losses by leaching and volatilization. Warming stimulated turnover of organic nitrogen further counteracts scarcity.The strengths of the land carbon feedbacks of the recent version of JSBACH, with βL = 0. 61 Pg ppm−1 and γL = −27. 5 Pg °C−1, are 34 and 53 % less than the averages of CMIP5 models, although the CMIP5 version of JSBACH simulated βL and γL, which are 59 and 42 % higher than multi-model average. These changes are primarily due to the new decomposition model, indicating the importance of soil organic matter decomposition for land carbon feedbacks.
Xu-Ri, Prentice IC, 2017, Modelling the demand for new nitrogen fixation by terrestrial ecosystems, Biogeosciences, Vol: 14, Pages: 2003-2017, ISSN: 1726-4170
Continual input of reactive nitrogen (N) is requiredto support the natural turnover of N in terrestrial ecosystems.This “N demand” can be satisfied in various ways, includingbiological N fixation (BNF) (the dominant pathway undernatural conditions), lightning-induced abiotic N fixation, Nuptake from sedimentary substrates, and N deposition fromnatural and anthropogenic sources. We estimated the globalnew N fixation demand (NNF), i.e. the total new N inputrequired to sustain net primary production (NPP) in nonagriculturalterrestrial ecosystems regardless of its origin,using a N-enabled global dynamic vegetation model (DyNLPJ).DyN-LPJ does not explicitly simulate BNF; rather, itestimates total NNF using a mass balance criterion and assumesthat this demand is met from one source or another.The model was run in steady state and then in transient modedriven by recent changes in CO2 concentration and climate.A range of values for key stoichiometric parameters was considered,based on recently published analyses. Modelled NPPand C : N ratios of litter and soil organic matter were consistentwith independent estimates. Modelled geographic patternsof ecosystem NNF were similar to other analyses, butactual estimated values exceeded recent estimates of globalBNF. The results were sensitive to a few key parameters: thefraction of litter carbon respired to CO2 during decompositionand plant-type-specific C : N ratios of litter and soil. Themodelled annual NNF increased by about 15 % during thecourse of the transient run, mainly due to increasing CO2concentration. The model did not overestimate recent terrestrialcarbon uptake, suggesting that the increase in NNF demandhas so far been met. Rising CO2 is further increasingthe NNF demand, while the future capacity of N sources tosupport this is unknown.
Rabin SS, Melton JR, Lasslop G, et al., 2017, The Fire Modeling Intercomparison Project (FireMIP), phase 1: experimental and analytical protocols with detailed model descriptions, GEOSCIENTIFIC MODEL DEVELOPMENT, Vol: 10, Pages: 1175-1197, ISSN: 1991-959X
The important role of fire in regulating vegetation community composition and contributions to emissions of greenhouse gases and aerosols make it a critical component of dynamic global vegetation models and Earth system models. Over 2 decades of development, a wide variety of model structures and mechanisms have been designed and incorporated into global fire models, which have been linked to different vegetation models. However, there has not yet been a systematic examination of how these different strategies contribute to model performance. Here we describe the structure of the first phase of the Fire Model Intercomparison Project (FireMIP), which for the first time seeks to systematically compare a number of models. By combining a standardized set of input data and model experiments with a rigorous comparison of model outputs to each other and to observations, we will improve the understanding of what drives vegetation fire, how it can best be simulated, and what new or improved observational data could allow better constraints on model behavior. In this paper, we introduce the fire models used in the first phase of FireMIP, the simulation protocols applied, and the benchmarking system used to evaluate the models. We have also created supplementary tables that describe, in thorough mathematical detail, the structure of each model.
Li G, Gerhart LM, Harrison SP, et al., 2017, Changes in biomass allocation buffer low CO2 effects on tree growth during the last glaciation, SCIENTIFIC REPORTS, Vol: 7, ISSN: 2045-2322
Isotopic measurements on junipers growing in southern California during the last glacial, when the ambient atmospheric [CO2] (ca) was ~180 ppm, show the leaf-internal [CO2] (ci) was approaching the modern CO2 compensation point for C3 plants. Despite this, stem growth rates were similar to today. Using a coupled light-use efficiency and tree growth model, we show that it is possible to maintain a stable ci/ca ratio because both vapour pressure deficit and temperature were decreased under glacial conditions at La Brea, and these have compensating effects on the ci/ca ratio. Reduced photorespiration at lower temperatures would partly mitigate the effect of low ci on gross primary production, but maintenance of present-day radial growth also requires a ~27% reduction in the ratio of fine root mass to leaf area. Such a shift was possible due to reduced drought stress under glacial conditions at La Brea. The necessity for changes in allocation in response to changes in [CO2] is consistent with increased below-ground allocation, and the apparent homoeostasis of radial growth, as ca increases today.
Davis T, Prentice IC, Stocker BD, et al., 2017, Simple process-led algorithms for simulating habitats (SPLASH v.1.0): robust indices of radiation, evapotranspiration and plant-available moisture, Geoscientific Model Development, Vol: 10, Pages: 689-708, ISSN: 1991-9603
Bioclimatic indices for use in studies of ecosystem function, species distribution, and vegetation dynamics under changing climate scenarios depend on estimates of surface fluxes and other quantities, such as radiation, evapotranspiration and soil moisture, for which direct observations are sparse. These quantities can be derived indirectly from meteorological variables, such as near-surface air temperature, precipitation and cloudiness. Here we present a consolidated set of simple process-led algorithms for simulating habitats (SPLASH) allowing robust approximations of key quantities at ecologically relevant timescales. We specify equations, derivations, simplifications, and assumptions for the estimation of daily and monthly quantities of top-of-the-atmosphere solar radiation, net surface radiation, photosynthetic photon flux density, evapotranspiration (potential, equilibrium, and actual), condensation, soil moisture, and runoff, based on analysis of their relationship to fundamental climatic drivers. The climatic drivers include a minimum of three meteorological inputs: precipitation, air temperature, and fraction of bright sunshine hours. Indices, such as the moisture index, the climatic water deficit, and the Priestley–Taylor coefficient, are also defined. The SPLASH code is transcribed in C++, FORTRAN, Python, and R. A total of 1 year of results are presented at the local and global scales to exemplify the spatiotemporal patterns of daily and monthly model outputs along with comparisons to other model results.
Dong N, Prentice IC, Evans BJ, et al., 2017, Leaf nitrogen from first principles: field evidence for adaptive variation with climate, Biogeosciences, Vol: 14, Pages: 481-495, ISSN: 1726-4189
Nitrogen content per unit leaf area (Narea) is a key variable in plant functional ecology and biogeochemistry. Narea comprises a structural component, which scales with leaf mass per area (LMA), and a metabolic component, which scales with Rubisco capacity. The co-ordination hypothesis, as implemented in LPJ and related global vegetation models, predicts that Rubisco capacity should be directly proportional to irradiance but should decrease with increases in ci : ca and temperature because the amount of Rubisco required to achieve a given assimilation rate declines with increases in both. We tested these predictions using LMA, leaf δ13C, and leaf N measurements on complete species assemblages sampled at sites on a north–south transect from tropical to temperate Australia. Partial effects of mean canopy irradiance, mean annual temperature, and ci : ca (from δ13C) on Narea were all significant and their directions and magnitudes were in line with predictions. Over 80 % of the variance in community-mean (ln) Narea was accounted for by these predictors plus LMA. Moreover, Narea could be decomposed into two components, one proportional to LMA (slightly steeper in N-fixers), and the other to Rubisco capacity as predicted by the co-ordination hypothesis. Trait gradient analysis revealed ci : ca to be perfectly plastic, while species turnover contributed about half the variation in LMA and Narea.
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