Observations of the uptake of carbonyl sulfide (COS) by trees under elevated atmospheric carbon dioxide concentrations

Global change forces ecosystems to adapt to elevated atmospheric concentrations of carbon dioxide (CO<sub>2</sub>). We understand that carbonyl sulfide (COS), a trace gas which is involved in building up the stratospheric sulfate aerosol layer, is taken up by vegetation with the same triad of the enzymes which are metabolizing CO<sub>2</sub>, i.e. ribulose-1,5-bisphosphate carboxylase/oxygenase (Rubisco), phosphoenolpyruvate carboxylase (PEP-Co) and carbonic anhydrase (CA). Therefore, we discuss a physiological/biochemical acclimation of these enzymes affecting the sink strength of vegetation for COS. We investigated the acclimation of two European tree species, <i>Fagus sylvatica</i> and <i>Quercus ilex</i>, grown inside chambers under elevated CO<sub>2</sub>, and determined the exchange characteristics and the content of CA after a 1–2 yr period of acclimation from 350 ppm to 800 ppm CO<sub>2</sub>. We demonstrate that a compensation point, by definition, does not exist. Instead, we propose to discuss a point of uptake affinity (PUA). The results indicate that such a PUA, the CA activity and the deposition velocities may change and may cause a decrease of the COS uptake by plant ecosystems, at least as long as the enzyme acclimation to CO<sub>2</sub> is not surpassed by an increase of atmospheric COS. As a consequence, the atmospheric COS level may rise causing an increase of the radiative forcing in the troposphere. However, this increase is counterbalanced by the stronger input of this trace gas into the stratosphere causing a stronger energy reflection by the stratospheric sulfur aerosol into space (Brühl et al., 2012). These data are very preliminary but may trigger a discussion on COS uptake acclimation to foster measurements with modern analytical instruments

Seasonal measurements of total OH reactivity emission rates from Norway spruce in 2011

Numerous reactive volatile organic compounds (VOCs) are emitted into the atmosphere by vegetation. Most biogenic VOCs are highly reactive towards the atmosphere's most important oxidant, the hydroxyl (OH) radical. One way to investigate the chemical interplay between biosphere and atmosphere is through the measurement of total OH reactivity, the total loss rate of OH radicals. This study presents the first determination of total OH reactivity emission rates (measurements via the comparative reactivity method) based on a branch cuvette enclosure system mounted on a Norway spruce (Picea abies) throughout spring, summer and autumn 2011. In parallel VOC emission rates were monitored by a second proton-transfer-reaction mass spectrometer (PTR-MS), and total ozone (O3) loss rates were obtained inside the cuvette. Total OH reactivity emission rates were in general temperature and light dependent, showing strong diel cycles with highest values during daytime. Monoterpene emissions contributed most, accounting for 56–69% of the measured total OH reactivity flux in spring and early summer. However, during late summer and autumn the monoterpene contribution decreased to 11–16%. At this time, a large missing fraction of the total OH reactivity emission rate (70–84%) was found when compared to the VOC budget measured by PTR-MS. Total OH reactivity and missing total OH reactivity emission rates reached maximum values in late summer corresponding to the period of highest temperature. Total O3 loss rates within the closed cuvette showed similar diel profiles and comparable seasonality to the total OH reactivity fluxes. Total OH reactivity fluxes were also compared to emissions from needle storage pools predicted by a temperature-only-dependent algorithm. Deviations of total OH reactivity fluxes from the temperature-only-dependent emission algorithm were observed for occasions of mechanical and heat stress. While for mechanical stress, induced by strong wind, measured VOCs could explain total OH reactivity emissions, during heat stress they could not. The temperature-driven algorithm matched the diel variation of total OH reactivity emission rates much better in spring than in summer, indicating a different production and emission scheme for summer and early autumn. During these times, unmeasured and possibly unknown primary biogenic emissions contributed significantly to the observed total OH reactivity flux

Seasonal measurements of total OH reactivity fluxes, total ozone loss rates and missing emissions from Norway spruce in 2011 [Discussion paper]

Numerous reactive volatile organic compounds (VOCs) are emitted into the atmosphere by vegetation. Most biogenic VOCs are highly reactive towards the atmosphere's most important oxidant, the hydroxyl (OH) radical. One way to investigate the chemical interplay between biosphere and atmosphere is through the measurement of total OH reactivity, the total loss rate of OH radicals. This study presents the first determination of total OH reactivity emission rates (measurements via the Comparative Reactivity Method) based on a branch cuvette enclosure system mounted on a Norway spruce (Picea abies) throughout spring, summer and autumn 2011. In parallel separate VOC emission rates were monitored by a Proton Transfer Reaction-Mass Spectrometer (PTR-MS), and total ozone (O3) loss rates were obtained inside the cuvette. Total OH reactivity emission rates were in general temperature and light dependent, showing strong diel cycles with highest values during daytime. Monoterpene emissions contributed most, accounting for 56–69% of the measured total OH reactivity flux in spring and early summer. However, during late summer and autumn the monoterpene contribution decreased to 11–16%. At this time, a large missing fraction of the total OH reactivity emission rate (70–84%) was found when compared to the VOC budget measured by PTR-MS. Total OH reactivity and missing total OH reactivity emission rates reached maximum values in late summer corresponding to the period of highest temperature. Total O3 loss rates within the closed cuvette showed similar diel profiles and comparable seasonality to the total OH reactivity fluxes. Total OH reactivity fluxes were also compared to emissions from needle storage pools predicted by a temperature-only dependent algorithm. Deviations of total OH reactivity fluxes from the temperature-only dependent emission algorithm were observed for occasions of mechanical and heat stress. While for mechanical stress, induced by strong wind, measured VOCs could explain total OH reactivity emissions, during heat stress they could not. The temperature driven algorithm matched the diel course much better in spring than in summer, indicating a different production and emission scheme for summer and early autumn. During these times, unmeasured and possibly unknown primary biogenic emissions contributed significantly to the observed total OH reactivity flux

The effect of flooding on the exchange of the volatile C₂-compounds ethanol, acetaldehyde and acetic acid between leaves of Amazonian floodplain tree species and the atmosphere

The effect of root inundation on the leaf emissions of ethanol, acetaldehyde and acetic acid in relation to assimilation and transpiration was investigated with 2–3 years old tree seedlings of four Amazonian floodplain species by applying dynamic cuvette systems under greenhouse conditions. Emissions were monitored over a period of several days of inundation using a combination of Proton Transfer Reaction Mass Spectrometry (PTR-MS) and conventional techniques (HPLC, ion chromatography). Under non-flooded conditions, none of the species exhibited measurable emissions of any of the compounds, but rather low deposition of acetaldehyde and acetic acid was observed instead. Tree species specific variations in deposition velocities were largely due to variations in stomatal conductance. Flooding of the roots resulted in leaf emissions of ethanol and acetaldehyde by all species, while emissions of acetic acid were only observed from the species exhibiting the highest ethanol and acetaldehyde emission rates. All three compounds showed a similar diurnal emission profile, each displaying an emission burst in the morning, followed by a decline in the evening. This concurrent behavior supports the conclusion, that all three compounds emitted by the leaves are derived from ethanol produced in the roots by alcoholic fermentation, transported to the leaves with the transpiration stream and finally partly converted to acetaldehyde and acetic acid by enzymatic processes. Co-emissions and peaking in the early morning suggest that root ethanol, after transportation with the transpiration stream to the leaves and enzymatic oxidation to acetaldehyde and acetate, is the metabolic precursor for all compounds emitted, though we can not totally exclude other production pathways. Emission rates substantially varied among tree species, with maxima differing by up to two orders of magnitude (25–1700 nmol m−2 min−1 for ethanol and 5–500 nmol m−2 min−1 for acetaldehyde). Acetic acid emissions reached 12 nmol m−2 min−1. The observed differences in emission rates between the tree species are discussed with respect to their root adaptive strategies to tolerate long term flooding, providing an indirect line of evidence that the root ethanol production is a major factor determining the foliar emissions. Species which develop morphological root structures allowing for enhanced root aeration produced less ethanol and showed much lower emissions compared to species which lack gas transporting systems, and respond to flooding with substantially enhanced fermentation rates and a non-trivial loss of carbon to the atmosphere. The pronounced differences in the relative emissions of ethanol to acetaldehyde and acetic acid between the tree species indicate that not only the ethanol production in the roots but also the metabolic conversion in the leaf is an important factor determining the release of these compounds to the atmosphere

High Performance Liquid Chromatography of Molecular Species from Free Sterols and Sterylglycosides Isolated from Oat Leaves and Seeds

Free sterols and sterylglycosides (SG) from oat leaves and seeds were isolated by conventional thin layer chromatography (TLC) and subjected to high performance liquid chromatography (HPLC) for resolution of molecular species. Acylsterylglycosides, isolated by TLC, were converted to SG by mild alkaline hydrolysis and determined as SG. Sterols and SG were injected onto the column without any chemical treatment and the separated species were detected at 200 nm. The separation of SG-species follows exactly the separation of free sterols. Though gas liquid chromatography still is the method of choice, advantages of HPLC is to analyse directly the SG-species without hydrolysis and derivatization as compared to GLC. After TLC the sterol- and the SG-fraction are injected directly onto the column. This is extremely important for labile sterylglycosides or sterols, as demonstrated for the avenasterol

Robust Generalised Linear Regression Models in Genetic Studies: Assessment of Standard Techniques and Their Generalisation to Incorporate Hampel's Function

In genetic studies, data under investigation exhibit a high-dimensionality, i.e., there are many more independent variables than measured individuals. In high-dimensional data, one expects observations departing from the majority of the data (so-called outliers). Such outliers can seriously affect statistical results because applied approaches using maximum likelihood estimation can be strongly biased by outliers. Robust approaches account for such outliers by assigning a weight to each observation, thus controlling their impact. However, these approaches are only rarely used in genetic studies. In this thesis, benefits and limitations of robust (generalised) linear models in comparison to the standard maximum likelihood approaches were investigated. For this purpose, an existing robust generalised linear model framework was generalised to incorporate another weighting function. In a first set of analyses, several already existing standard and robust approaches for linear, Poisson as well as logistic regression were compared. There, the attention was drawn to model selection consistency and prediction accuracy, the influence of a single outlier and the influence of genotyping errors on estimates. The prediction accuracy was similar for (robust) linear and (robust) Poisson regression models in a real data application. In view of model selection consistency, Poisson regression selected two or three independent variables whereas linear regression always included the same single independent variable, which was, however, in common for all regression methods. These results were complemented by an inclusion of different independent variables into the standard and robust logistic regression models in a second real data application. Within this application, it was observed that robust logistic regression better controlled the outlier influence. A simulation study revealed a decreasing influence of genotyping errors on estimates with increasing causal allele carrier frequencies. Furthermore, there was an indication of a possible benefit of robust logistic regression. At the time of method application, the robust generalised linear model framework only provided the bounded Huber function for observation weighting. In this thesis, the re-descending Hampel function was incorporated into this framework for logistic and Poisson regression by explicit calculations for the Fisher consistency correction and for the asymptotic variance as well as by adaptation of the existing source code. In a second set of analyses, the developed approach for robust logistic regression was compared against the standard and the existing robust logistic regression methods based on simulated and real data -- both dealing with an (indirect) association analysis. In the simulation study, several populations were simulated assuming different penetrance models, minor allele frequencies, genotyping error rates and linkage between causal and marker allele locus. In the analysis, the attention was drawn to several statistical properties comprising mean squared error of the estimates, statistical power and type I error rate. In the simulation study, all approaches controlled the type I error rate. Based on the results of the statistical properties investigation, a method recommendation must depend on the aim of the analysis. To reach a large power for variant identification, standard logistic regression would be an adequate choice. If a small mean squared error probably avoiding a strong effect overestimation was the goal, robust logistic regression represented a valuable alternative to the standard approach. This especially held when analysing rare variants or assuming a recessive penetrance model both leading to a low probability to observe the causal genotype. If extreme outliers are expected in the data, the re-descending Hampel function should be favoured. The aim for future work should be the examination of statistical properties (mean squared error, statistical power, type I error rate) of robust Poisson regression and of the robust hurdle model arising by the combination of the logistic and the truncated Poisson model – both applying the Hampel function. Additionally, an inclusion of further weighting functions as well as additional distributions would be of great interest for a broader application range and the chance of a power gain for robust regression methods. Summarising, the coincidence of expected outliers and observed rare events in high-dimensional data challenges the analysis of genetic data. The results of this thesis indicate that these analyses can benefit from the application of robust logistic regression models to narrow down the winner's curse of rare and recessive susceptibility variants