A unifying conceptual model for the environmental responses of isoprene emissions from plants

This is the final version of the article. Available from the publisher via the DOI in this record.BACKGROUND AND AIMS: Isoprene is the most important volatile organic compound emitted by land plants in terms of abundance and environmental effects. Controls on isoprene emission rates include light, temperature, water supply and CO2 concentration. A need to quantify these controls has long been recognized. There are already models that give realistic results, but they are complex, highly empirical and require separate responses to different drivers. This study sets out to find a simpler, unifying principle. METHODS: A simple model is presented based on the idea of balancing demands for reducing power (derived from photosynthetic electron transport) in primary metabolism versus the secondary pathway that leads to the synthesis of isoprene. This model's ability to account for key features in a variety of experimental data sets is assessed. KEY RESULTS: The model simultaneously predicts the fundamental responses observed in short-term experiments, namely: (1) the decoupling between carbon assimilation and isoprene emission; (2) a continued increase in isoprene emission with photosynthetically active radiation (PAR) at high PAR, after carbon assimilation has saturated; (3) a maximum of isoprene emission at low internal CO2 concentration (ci) and an asymptotic decline thereafter with increasing ci; (4) maintenance of high isoprene emissions when carbon assimilation is restricted by drought; and (5) a temperature optimum higher than that of photosynthesis, but lower than that of isoprene synthase activity. CONCLUSIONS: A simple model was used to test the hypothesis that reducing power available to the synthesis pathway for isoprene varies according to the extent to which the needs of carbon assimilation are satisfied. Despite its simplicity the model explains much in terms of the observed response of isoprene to external drivers as well as the observed decoupling between carbon assimilation and isoprene emission. The concept has the potential to improve global-scale modelling of vegetation isoprene emission.We thank Karena McKinney for providing the original isoprene data for the Harvard forest site. We thank Russell Monson and Ru¨diger Grote for their helpful and constructive comments on the manuscript. C.M. and I.C.P. have received funding from the European Community’s Seventh Framework Programme (FP7 2007 – 2013) under grant agreement no. 238366

Reconciling the optimal and empirical approaches to modelling stomatal conductance

Models of vegetation function are widely used to predict the effects of climate change on carbon, water and nutrient cycles of terrestrial ecosystems, and their feedbacks to climate. Stomatal conductance, the process that governs plant water use and carbon uptake, is fundamental to such models. In this paper, we reconcile two long-standing theories of stomatal conductance. The empirical approach, which is most commonly used in vegetation models, is phenomenological, based on experimental observations of stomatal behaviour in response to environmental conditions. The optimal approach is based on the theoretical argument that stomata should act to minimize the amount of water used per unit carbon gained. We reconcile these two approaches by showing that the theory of optimal stomatal conductance can be used to derive a model of stomatal conductance that is closely analogous to the empirical models. Consequently, we obtain a unified stomatal model which has a similar form to existing empirical models, but which now provides a theoretical interpretation for model parameter values. The key model parameter, g1, is predicted to increase with growth temperature and with the marginal water cost of carbon gain. The new model is fitted to a range of datasets ranging from tropical to boreal trees. The parameter g1 is shown to vary with growth temperature, as predicted, and also with plant functional type. The model is shown to correctly capture responses of stomatal conductance to changing atmospheric CO2, and thus can be used to test for stomatal acclimation to elevated CO2. The reconciliation of the optimal and empirical approaches to modelling stomatal conductance is important for global change biology because it provides a simple theoretical framework for analyzing, and simulating, the coupling between carbon and water cycles under environmental change. © 2011 Blackwell Publishing Ltd

Global environmental controls of wildfire burnt area, size and intensity.

Fire is an important influence on the global patterns of vegetation structure and composition. Wildfire is included as a distinct process in many dynamic global vegetation models but limited current understanding of fire regimes restricts these models' ability to reproduce more than the broadest geographic patterns. Here we present a statistical analysis of the global controls of remotely sensed burnt area (BA), fire size (FS), and a derived metric related to fire intensity (FI). Separate generalized linear models were fitted to observed monthly fractional BA from the Global Fire Emissions Database (GFEDv4), median FS from the Global Fire Atlas, and median fire radiative power from the MCD14ML dataset normalized by the square root of median FS. The three models were initially constructed from a common set of 16 predictors; only the strongest predictors for each model were retained in the final models. It is shown that BA is primarily driven by fuel availability and dryness; FS by conditions promoting fire spread; and FI by fractional tree cover and road density. Both BA and FS are constrained by landscape fragmentation, whereas FI is constrained by fuel moisture. Ignition sources (lightning and human population) were positively related to BA (after accounting for road density), but negatively to FI. These findings imply that the different controls on BA, FS and FI need to be considered in process-based models. They highlight the need to include measures of landscape fragmentation as well as fuel load and dryness, and to pay close attention to the controls of fire spread

Accounting for atmospheric carbon dioxide variations in pollen-based reconstructions of past hydroclimates.

Changes in atmospheric carbon dioxide (CO2) concentration directly influence the ratio of stomatal water loss to carbon uptake. This ratio (e) is a fundamental quantity for terrestrial ecosystems, as it defines the water requirement for plant growth. Statistical and analogue-based methods used to reconstruct past hydroclimate variables from fossil pollen assemblages do not take account of the effect of CO2 variations on e. Here we present a general, globally applicable method to correct for this effect. The method involves solving an equation that relates e to a climatic moisture index (MI, the ratio of mean annual precipitation to mean annual potential evapotranspiration), mean growing-season temperature, and ambient CO2. The equation is based on the least-cost optimality hypothesis, which predicts how the ratio (χ) of leaf-internal to ambient CO2 varies with vapour pressure deficit (vpd), growing-season temperature and atmospheric pressure, combined with experimental evidence on the response of χ to the CO2 level at which plants have been grown. An empirical relationship based on global climate data is used to relate vpd to MI and growing-season temperature. The solution to the equation allows past MI to be estimated from pollen-reconstructed MI, given past CO2 and temperature. This MI value can be used to estimate mean annual precipitation, accounting for the effects of orbital variations, temperature and cloud cover (inferred from MI) on potential evapotranspiration. A pollen record from semi-arid Spain that spans the last glacial interval is used to illustrate the method. Low CO2 leads to estimated MI being larger than reconstructed MI during glacial times. The CO2 effect on inferred precipitation was partly offset by increased cloud cover; nonetheless, inferred precipitation was greater than present almost throughout the glacial period. This method allows a more robust reconstruction of past hydroclimatic variations than currently available tools

Optimality-based modelling of wheat sowing dates globally

CONTEXT Sowing dates are currently an essential input for crop models. However, in the future, the optimal sowing time will be affected by climate changes and human adaptations to these changes. A better understanding of what determines the choice of wheat type and sowing dates is required to be able to predict future crop yields reliably. OBJECTIVE This study was conducted to understand how climate conditions affect the choice of wheat types and sowing dates globally. METHODS We develop a model integrating optimality concepts for simulating gross primary production (GPP) with climate constraints on wheat phenology to predict sowing dates. We assume that wheat could be sown at any time with suitable climate conditions and farmers would select a sowing date that maximises yields. The model is run starting on every possible climatically suitable day, determined by climate constraints associated with low temperature and intense precipitation. The optimal sowing date is the day which gives the highest yield in each location. We evaluate the simulated optimal sowing dates with data on observed sowing dates created by merging census-based datasets and local agronomic information, then predict their changes under future climate scenarios to gain insight into the impacts of climate change. RESULTS AND CONCLUSIONS Cold-season temperatures are the major determinant of sowing dates in the extra-tropics, whereas the seasonal cycle of monsoon rainfall is important in the tropics. Our model captures the timing of reported sowing dates, with differences of less than one month over much of the world; maximum errors of up to two months occur in tropical regions with large altitudinal gradients. Discrepancies between predictions and observations are larger in tropical regions than temperate and cold regions. Slight warming is shown to promote earlier sowing in wet areas but later in dry areas; larger warming leads to delayed sowing in most regions. These predictions arise due to the interactions of several influences on yield, including the effects of warming on growing-season length, the need for sufficient moisture during key phenological stages, and the temperature threshold for vernalization of winter wheat. SIGNIFICANCE The integration of optimality concepts for simulating GPP with climate constraints on phenology provides realistic predictions of wheat type and sowing dates. The model thus provides a basis for predicting how crop calendars might change under future climate change. It can also be used to investigate potential changes in management to mitigate the negative impacts of climate change

Leaf morphological traits as adaptations to multiple climate gradients

1. Leaf morphological traits vary systematically along climatic gradients. However, recent studies in plant functional ecology have mainly analysed quantitative traits, while numerical models of species distributions and vegetation function have focused on traits associated with resource acquisition; both ignore the wider functional significance of leaf morphology. 2. A data set comprising 22 leaf morphological traits for 662 woody species from 92 sites, representing all biomes present in China, was subjected to multivariate analysis in order to identify leading dimensions of trait covariation (correspondence analysis), quantify climatic and phylogenetic contributions (canonical correspondence analysis with variation partitioning), and characterize co-occurring trait syndromes (k-means clustering) and their climatic preferences. 3. Three axes accounted for > 20% of trait variation in both evergreen and deciduous species. Moisture index, precipitation seasonality and growing-season temperature accounted for 8–10% of trait variation; family 15–32%. Microphyll or larger, mid- to dark green leaves with drip-tips in wetter climates contrasted with nanophyll or smaller glaucous leaves without drip-tips in drier climates. Thick, entire leaves in less seasonal climates contrasted with thin, marginal dissected, aromatic, and involute/revolute leaves in more seasonal climates. Thick, involute, hairy leaves in colder climates contrasted with thin leaves with marked surface structures (surface patterning) in warmer climates. Distinctive trait clusters were linked to the driest and most seasonal climates, for example the clustering of picophyll, fleshy and succulent leaves in the driest climates and leptophyll, linear, dissected, revolute or involute, and aromatic leaves in regions with highly seasonal rainfall. Several trait clusters co-occurred in wetter climates, including clusters characterised by microphyll, moderately thick, patent, and entire leaves or notophyll, waxy, dark green leaves. 4. Synthesis. The plastic response of size, shape, color and other leaf morphological traits to climate is muted, thus their apparent shift along climate gradients reflects plant adaptations to environment at a community-level as determined by species replacement. Information on leaf morphological traits, widely available in floras, could be used to strengthen predictive models of species distribution and vegetation function

Environmental controls on the light use efficiency of terrestrial gross primary production

Gross primary production (GPP) by terrestrial ecosystems is a key quantity in the global carbon cycle. The instantaneous controls of leaf-level photosynthesis are well established, but there is still no consensus on the mechanisms by which canopy-level GPP depends on spatial and temporal variation in the environment. The standard model of photosynthesis provides a robust mechanistic representation for C3 species; however, additional assumptions are required to “scale up” from leaf to canopy. As a consequence, competing models make inconsistent predictions about how GPP will respond to continuing environmental change. This problem is addressed here by means of an empirical analysis of the light use efficiency (LUE) of GPP inferred from eddy covariance carbon dioxide flux measurements, in situ measurements of photosynthetically active radiation (PAR), and remotely sensed estimates of the fraction of PAR (fAPAR) absorbed by the vegetation canopy. Focusing on LUE allows potential drivers of GPP to be separated from its overriding dependence on light. GPP data from over 100 sites, collated over 20 years and located in a range of biomes and climate zones, were extracted from the FLUXNET2015 database and combined with remotely sensed fAPAR data to estimate daily LUE. Daytime air temperature, vapor pressure deficit, diffuse fraction of solar radiation, and soil moisture were shown to be salient predictors of LUE in a generalized linear mixed-effects model. The same model design was fitted to site-based LUE estimates generated by 16 terrestrial ecosystem models. The published models showed wide variation in the shape, the strength, and even the sign of the environmental effects on modeled LUE. These findings highlight important model deficiencies and suggest a need to progress beyond simple “goodness of fit” comparisons of inferred and predicted carbon fluxes toward an approach focused on the functional responses of the underlying dependencies

Holocene vegetation dynamics of the Eastern Mediterranean region: old controversies addressed by a new analysis

Aim: We reconstruct vegetation changes since 12 ky in the Eastern Mediterranean to examine four features of the regional vegetation history that are controversial: the extent of non-analogue vegetation assemblages in the transition from the Late Glacial to the early Holocene, the synchroneity of postglacial forest expansion, the geographical extent of temperate deciduous forest during the mid-Holocene and the timing and trigger for the re-establishment of drought-tolerant vegetation during the late Holocene. Location: The Eastern Mediterranean–Black Sea Caspian Corridor. Taxon: Vascular plants. Methods: We reconstruct vegetation changes for 122 fossil pollen records using a method that accounts for within-biome variability in pollen taxon abundance to determine the biome with which a sample has greatest affinity. Per-biome affinity threshold values were used to identify samples that do not belong to any modern biome. We apply time series analysis and mapping to examine space and time changes. Results: Sites with non-analogue vegetation were most common between 11.5 and 9.5 ky and mostly in the Carpathians. The transition from open vegetation to forest occurred at 10.64 ± 0.65 ky across the whole region. Temperate deciduous forest was not more extensive at 6 ky; maximum expansion occurred between 5.5 and 5 ky. Expansion of forest occurred between c. 4 and 2.8 k, followed by an abrupt decrease and a subsequent recovery. This pattern is not consistent with a systematic decline of forest towards more drought-tolerant vegetation in the late Holocene but is consistent with centennial-scale speleothem patterns linked to variations in moisture availability. Main Conclusions: We show the occurrence of non-analogue vegetation types peaked during early Holocene, forest expansion was synchronous across the region and there was an expansion of moisture-demanding temperate trees around 5.5 to 5 ky. There is no signal of a continuous late Holocene aridification, but changes in forest cover appear to reflect climatic rather than anthropogenic influences

The Royal Society Climate Updates: What have we learnt since the IPCC 5th Assessment Report?

Climate has a huge influence on the way we live. For example, it affects the crops we can grow and the diseases we might encounter in particular locations. It also determines the physical infrastructure we need to build to survive comfortably in the face of extremes of heat, cold, drought and flood. Human emissions of carbon dioxide and other greenhouse gases have changed the composition of the atmosphere over the last two centuries. This is expected to take Earth’s climate out of the relatively stable range that has characterised the last few thousand years, during which human society has emerged. Measurements of ice cores and sea-floor sediments show that the current concentration of carbon dioxide, at just over 400 parts per million, has not been experienced for at least three million years. This causes more of the heat from the Sun to be retained on Earth, warming the atmosphere and ocean. The global average of atmospheric temperature has so far risen by about 1˚C compared to the late 19th century, with further increases expected dependent on the trajectory of carbon dioxide emissions in the next few decades. In 2013 and 2014 the Intergovernmental Panel on Climate Change (IPCC) published its fifth assessment report (AR5) assessing the evidence about climate change and its impacts. This assessment considered data from observations and records of the past. It then assessed future changes and impacts based on various scenarios for emissions of greenhouse gases and other anthropogenic factors. In 2015, almost every nation in the world agreed (in the so-called Paris Agreement) to the challenging goal of keeping global average warming to well below 2°C above pre-industrial temperatures while pursuing efforts to limit it to 1.5°C. With the next assessment report (AR6) not due until 2022, it is timely to consider how evidence presented since the publication of AR5 affects the assessments made then. The Earth’s climate is a complex system. To understand it, and the impact that climate change will have, requires many different kinds of study. Climate science consists of theory, observation and modelling. Theory begins with well-established scientific principles, seeks to understand processes occurring over a range of spatial and temporal scales and provides the basis for models. Observation includes long time series of careful measurements, recent data from satellites, and studies of past climate using archives such as tree rings, ice cores and marine sediments. It also encompasses laboratory and field experiments designed to test and enhance understanding of processes. Computer models of the Earth climate system use theory, calibrated and validated by the observations, to calculate the result of future changes. There are nevertheless uncertainties in estimating future climate. Firstly the course of climate change is dependent on what socioeconomic, political and energy paths society takes. Secondly there remain inevitable uncertainties induced for example by variability in the interactions between different parts of the Earth system and by processes, such as cloud formation, that occur at too small a scale to incorporate precisely in global models. Assessments such as those of the IPCC describe the state of knowledge at a particular time, and also highlight areas where more research is needed. We are still exploring and improving our understanding of many of the processes within the climate system, but, on the whole, new research confirms the main ideas underpinning climate research, while refining knowledge, so as to reduce the uncertainty in the magnitude and extent of crucial impacts

Global leaf-trait mapping based on optimality theory

Aim Leaf traits are central to plant function, and key variables in ecosystem models. However recently published global trait maps, made by applying statistical or machine-learning techniques to large compilations of trait and environmental data, differ substantially from one another. This paper aims to demonstrate the potential of an alternative approach, based on eco-evolutionary optimality theory, to yield predictions of spatio-temporal patterns in leaf traits that can be independently evaluated. Innovation Global patterns of community-mean specific leaf area (SLA) and photosynthetic capacity (Vcmax) are predicted from climate via existing optimality models. Then leaf nitrogen per unit area (Narea) and mass (Nmass) are inferred using their (previously derived) empirical relationships to SLA and Vcmax. Trait data are thus reserved for testing model predictions across sites. Temporal trends can also be predicted, as consequences of environmental change, and compared to those inferred from leaf-level measurements and/or remote-sensing methods, which are an increasingly important source of information on spatio-temporal variation in plant traits. Main conclusions Model predictions evaluated against site-mean trait data from > 2,000 sites in the Plant Trait database yielded R2 = 73% for SLA, 38% for Nmass and 28% for Narea. Declining species-level Nmass, and increasing community-level SLA, have both been recently reported and were both correctly predicted. Leaf-trait mapping via optimality theory holds promise for macroecological applications, including an improved understanding of community leaf-trait responses to environmental change