AIM: Functional traits of organisms are key to understanding and predicting biodiversity and ecological change, which motivates continuous collection of traits and their integration into global ...databases. Such trait matrices are inherently sparse, severely limiting their usefulness for further analyses. On the other hand, traits are characterized by the phylogenetic trait signal, trait–trait correlations and environmental constraints, all of which provide information that could be used to statistically fill gaps. We propose the application of probabilistic models which, for the first time, utilize all three characteristics to fill gaps in trait databases and predict trait values at larger spatial scales. INNOVATION: For this purpose we introduce BHPMF, a hierarchical Bayesian extension of probabilistic matrix factorization (PMF). PMF is a machine learning technique which exploits the correlation structure of sparse matrices to impute missing entries. BHPMF additionally utilizes the taxonomic hierarchy for trait prediction and provides uncertainty estimates for each imputation. In combination with multiple regression against environmental information, BHPMF allows for extrapolation from point measurements to larger spatial scales. We demonstrate the applicability of BHPMF in ecological contexts, using different plant functional trait datasets, also comparing results to taking the species mean and PMF. MAIN CONCLUSIONS: Sensitivity analyses validate the robustness and accuracy of BHPMF: our method captures the correlation structure of the trait matrix as well as the phylogenetic trait signal – also for extremely sparse trait matrices – and provides a robust measure of confidence in prediction accuracy for each missing entry. The combination of BHPMF with environmental constraints provides a promising concept to extrapolate traits beyond sampled regions, accounting for intraspecific trait variability. We conclude that BHPMF and its derivatives have a high potential to support future trait‐based research in macroecology and functional biogeography.
Full text
Available for:
BFBNIB, FZAB, GIS, IJS, KILJ, NLZOH, NMLJ, NUK, OILJ, PNG, SAZU, SBCE, SBMB, UL, UM, UPUK
Understanding the role of climate extremes and their impact on the carbon (C) cycle is increasingly a focus of Earth system science. Climate extremes such as droughts, heat waves, or heavy ...precipitation events can cause substantial changes in terrestrial C fluxes. On the other hand, extreme changes in C fluxes are often, but not always, driven by extreme climate conditions. Here we present an analysis of how extremes in temperature and precipitation, and extreme changes in terrestrial C fluxes are related to each other in 10 state‐of‐the‐art terrestrial carbon models, all driven by the same climate forcing. We use model outputs from the North American Carbon Program Multi‐scale Synthesis and Terrestrial Model Intercomparison Project (MsTMIP). A global‐scale analysis shows that both droughts and heat waves translate into anomalous net releases of CO2 from the land surface via different mechanisms: Droughts largely decrease gross primary production (GPP) and to a lower extent total respiration (TR), while heat waves slightly decrease GPP but increase TR. Cold and wet periods have a smaller opposite effect. Analyzing extremes in C fluxes reveals that extreme changes in GPP and TR are often caused by strong shifts in water availability, but for extremes in TR shifts in temperature are also important. Extremes in net CO2 exchange are equally strongly driven by deviations in temperature and precipitation. Models mostly agree on the sign of the C flux response to climate extremes, but model spread is large. In tropical forests, C cycle extremes are driven by water availability, whereas in boreal forests temperature plays a more important role. Models are particularly uncertain about the C flux response to extreme heat in boreal forests.
Key Points
Models largely agree on the sign of the carbon flux response to climate extremes
Models are uncertain in the carbon flux response to heat waves in boreal forests
Droughts and heat waves strongly compound each other in their impact on C fluxes
Full text
Available for:
FZAB, GIS, IJS, KILJ, NLZOH, NUK, OILJ, SBCE, SBMB, UL, UM, UPUK
Latent and sensible heat flux observations are essential for understanding land–atmosphere interactions. Measurements from the eddy covariance technique are widely used but suffer from systematic ...energy imbalance problems, partly due to missing large eddies from sub‐mesoscale transport. Because available energy drives the development of large eddies, we propose an available energy based correction method for turbulent flux measurements. We apply our method to 172 flux tower sites and show that we can reduce the energy imbalance from −14.99 to −0.65 W m−2 on average, together with improved consistency between turbulent fluxes and available energy and associated increases in r2 at individual sites and across networks. Our results suggest that our method is conceptually and empirically preferable over the method implemented in the ONEFlux processing. This can contribute to the efforts in understanding and addressing the energy imbalance issue, which is crucial for the evaluation and calibration of land surface models.
Plain Language Summary
Eddy covariance measurements are key to understanding the exchange of energy and water between the Earth's surface and the atmosphere, which helps us validate Earth system models that predict how the land interacts with the atmosphere. However, these measurements often show an energy imbalance problem, meaning that the measured turbulent energy does not fully account for all the energy entering the system. For two decades, scientists have been using advanced simulations and multi‐tower measurements to find out why this happens, and have found that the movements of airflow in a horizontal direction play a large role. Taking this knowledge into account, we propose a simple, data‐driven method to make these measurements more accurate. This new approach reduces the error not just at one eddy covariance site, but at multiple sites around the globe, and it's also effective at reflecting the energy changes that occur with daily weather events like rain.
Key Points
Observed systematic imbalance of energy flux (∼17%) across the network of eddy covariance sites
A theoretically motivated correction method based on available energy variations is proposed
The available energy correction method has conceptual and empirical advantages compared to the method implemented in the ONEFlux pipeline
Full text
Available for:
FZAB, GIS, IJS, KILJ, NLZOH, NUK, OILJ, SBCE, SBMB, UL, UM, UPUK
This article summarizes the changes in landscape structure because of human land management over the last several centuries, and using observed and modeled data, documents how these changes have ...altered biogeophysical and biogeochemical surface fluxes on the local, mesoscale, and regional scales. Remaining research issues are presented including whether these landscape changes alter large‐scale atmospheric circulation patterns far from where the land use and land cover changes occur. We conclude that existing climate assessments have not yet adequately factored in this climate forcing. For those regions that have undergone intensive human landscape change, or would undergo intensive change in the future, we conclude that the failure to factor in this forcing risks a misalignment of investment in climate mitigation and adaptation. WIREs Clim Change 2011, 2:828–850. doi: 10.1002/wcc.144
This article is categorized under:
Paleoclimates and Current Trends > Climate Forcing
Full text
Available for:
BFBNIB, FZAB, GIS, IJS, KILJ, NLZOH, NUK, OILJ, SBCE, SBMB, UL, UM, UPUK
Abstract
Extreme hydrological and meteorological conditions can severely affect ecosystems, parts of the economy, and consequently society. These impacts are expected to be aggravated by climate ...change. Here we analyze and compare the impacts of multiple types of extreme events across several domains in Europe, to reveal corresponding impact signatures. We characterize the distinct impacts of droughts, floods, heat waves, frosts and storms on a variety of biophysical and social variables at national level and half-monthly time scale. We find strong biophysical impacts of droughts, floods, heat waves and frosts, while public attention and property damage are more affected by storms and floods. We show unexpected impact patterns such as reduced human mortality during floods and storms. Comparing public attention anomalies with impacts across all other considered domains we find that attention on droughts is comparatively low despite the significant overall impacts. Resolving these impact patterns highlights large-scale vulnerability and supports regional extreme event management to consequently reduce disaster risks.
According to experimental studies, plant emissions of volatile organic compounds (VOC) are controlled by stomata to a varying extent, but the differing responses could not be explained so far. A ...dynamic emission model developed in a previous study indicated that stomata may limit the emission rate in a nonsteady state conditions, whereas the rate of increase of liquid‐phase volatile concentrations controls the degree to which stomata temporarily curtail the emission. Despite its large predictive capability, potentially large number of volatile physico‐chemical and leaf structural variables are needed for parameterization of such dynamic models, limiting the usefulness of the approach. We conducted a sensitivity analysis to determine the effect of varying VOC distribution between gas‐ and liquid‐phases (Henry's law constant, H, Pa m3 mol−1) and varying internal diffusion conductances in the liquid‐ and gas‐phases. The model was parameterized for three contrasting leaf architectures (conifer, sclerophyll, and mesophytic leaves). The sensitivity analysis indicated that the volatile H value is the key variable affecting the stomatal sensitivity of VOC emissions. Differences in leaf architecture, in particular in leaf liquid volume to area ratio, also modified the emission responses to changes in stomatal aperture, but these structural effects were superimposed by compound gas/liquid phase partitioning. The results of this analysis indicate that major effort in parameterization of dynamic VOC emission models should be directed toward obtaining reliable gas/liquid‐phase equilibria for various plant volatiles, and that these models may readily be applied for leaves with contrasting architecture.
Volatile (VOC) flux from leaves may be expressed as GSΔP, where GS is stomatal conductance to specific compound and ΔP partial pressure gradient between the atmosphere and substomatal cavities. It ...has been suggested that decreases in GS are balanced by increases in ΔP such that stomata cannot control VOC emission. Yet, responses of emission rates of various volatiles to experimental manipulations of stomatal aperture are contrasting. To explain these controversies, a dynamic emission model was developed considering VOC distribution between gas and liquid phases using Henry's law constant (H, Pa m3 mol−1). Our analysis demonstrates that highly volatile compounds such as isoprene and monoterpenes with H values on the order of 103 have gas and liquid pool half‐times of a few seconds, and thus cannot be controlled by stomata. More soluble compounds such as alcohols and carboxylic acids with H values of 10−2–101 are controlled by stomata with the degree of stomatal sensitivity varying with H. Inability of compounds with high solubility to support a high partial pressure, and thus to balance ΔP in response to a decrease in GS is the primary explanation for different stomatal sensitivities. For compounds with low H, the analysis predicts bursts of emission after stomatal opening that accord with experimental observations, but that cannot be currently explained. Large within‐leaf VOC pool sizes in compounds with low H also increase the system inertia to environmental fluctuations. In conclusion, dynamic models are necessary to simulate diurnal variability of the emissions of compounds that preferably partition to aqueous phase.
It has only been recognized relatively recently that biological processes can control and steer the Earth system in a globally significant way. Recent evidence suggests that, on a global scale, ...terrestrial ecosystems will provide a positive feedback in a warming world, albeit of uncertain magnitude.
Full text
Available for:
DOBA, IJS, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
The intensification of the hydrological cycle, with an observed and modeled increase in drought incidence and severity, underscores the need to quantify drought effects on carbon cycling and the ...terrestrial sink. FLUXNET, a global network of eddy covariance towers, provides dense data streams of meteorological data, and through flux partitioning and gap filling algorithms, estimates of net ecosystem productivity (FNEP), gross ecosystem productivity (P), and ecosystem respiration (R). We analyzed the functional relationship of these three carbon fluxes relative to evaporative fraction (EF), an index of drought and site water status, using monthly data records from 238 micrometeorological tower sites distributed globally across 11 biomes. The analysis was based on relative anomalies of both EF and carbon fluxes and focused on drought episodes by biome and climatic season. Globally P was almost equal to50% more sensitive to a drought event than R. Network-wide drought-induced decreases in carbon flux averaged -16.6 and -9.3 g C m⁻² month⁻¹ for P and R, i.e., drought events induced a net decline in the terrestrial sink. However, in evergreen forests and wetlands drought was coincident with an increase in P or R during parts of the growing season. The most robust relationships between carbon flux and EF occurred during climatic spring for FNEP and in climatic summer for P and R. Upscaling flux sensitivities to a global map showed that spatial patterns for all three carbon fluxes were linked to the distribution of croplands. Agricultural areas exhibited the highest sensitivity whereas the tropical region had minimal sensitivity to drought. Combining gridded flux sensitivities with their uncertainties and the spatial grid of FLUXNET revealed that a more robust quantification of carbon flux response to drought requires additional towers in all biomes of Africa and Asia as well as in the cropland, shrubland, savannah, and wetland biomes globally.
Full text
Available for:
BFBNIB, FZAB, GIS, IJS, KILJ, NLZOH, NUK, OILJ, SBCE, SBMB, UL, UM, UPUK
Natural and anthropogenic disturbances are important drivers of tree mortality, shaping the structure, composition, and biomass distribution of forest ecosystems. Differences in disturbance regimes, ...characterized by the frequency, extent, and intensity of disturbance events, result in structurally different landscapes. In this study, we design a model‐based experiment to investigate the links between disturbance regimes and spatial biomass patterns. First, the effects of disturbance events on biomass patterns are simulated using a simple dynamic carbon cycle model based on different disturbance regime attributes, which are characterized via three parameters: μ (probability scale), α (clustering degree), and β (intensity slope). 856,800 dynamically stable biomass patterns were then simulated using combined disturbance regime, primary productivity, and background mortality. As independent variables, we use biomass synthesis statistics from simulated biomass patterns to retrieve three disturbance regime parameters. Results show confident inversion of all three “true” disturbance parameters, with Nash‐Sutcliffe efficiency of 94.8% for μ, 94.9% for α, and 97.1% for β. Biomass histogram statistics primarily dominate the prediction of μ and β, while texture features have a more substantial influence on α. Overall, these results demonstrate the association between biomass patterns and disturbance regimes. Given the increasing availability of Earth observation of biomass, our findings open a new avenue to understand better and parameterize disturbance regimes and their links with vegetation dynamics under climate change. Ultimately, at a large scale, this approach would improve our current understanding of controls and feedback at the biosphere‐atmosphere interface in the present Earth system models.
Plain Language Summary
Forest dynamics are shaped by different disturbances, which are challenging to monitor and predict. Identifying individual disturbance occurrences and their impact on forest carbon stocks (biomass) is complex. However, our study deciphers the characteristics of disturbance occurrence, that is, disturbance regime, from biomass pattern. We characterized this regime across three dimensions: extent (μ), frequency (α), and intensity (β). Through a 200‐year landscape experiment, we explored the synthetic dynamically stable biomass under different disturbance regimes. Statistical features from biomass simulations revealed distinct spatial patterns, forming a connection between these patterns and the disturbance regime parameters via machine learning. Notably, specific biomass pattern statistics influence distinct disturbance regime parameters: μ and β are linked to histogram stats, while α is tied to texture statistics. This approach establishes a framework to diagnose disturbance regimes from biomass patterns, offering a way to incorporate these regimes into Earth system models.
Key Points
We investigate the link between disturbance regimes and spatial patterns of aboveground biomass emerging from diverse primary productivity
The proposed framework allows for inferring disturbance probability, size and intensity from spatial features in aboveground biomass
Disturbance regimes from high‐res Earth observations can enhance carbon cycle dynamics prediction from interannual to longer time scales
Full text
Available for:
DOBA, FZAB, GIS, IJS, IZUM, KILJ, NLZOH, NUK, OILJ, PILJ, PNG, SAZU, SBCE, SBMB, SIK, UILJ, UKNU, UL, UM, UPUK
PDF
