Biology subjects

Christen, A.

Publications and source records attributed to Christen, A..

4 recordsLinked to original sources

bioRxiv2026

Seasonal dynamics and sun/shade heterogeneity of leaf gas exchange and VOC emissions inside a tall temperate forest canopy

Leaf gas exchange is the key driver of forest carbon uptake and directly determines forest carbon sink activity. Additionally, plants release a variety of biogenic volatile organic compounds (VOCs) acting as stress signals of trees. However, continuous hourly resolved measurements of leaf gas exchange and VOC emissions in tall tree canopies are challenging and remain scarce. To this end, we developed a sophisticated in-situ leaf gas exchange measurement system with 24 cuvettes deployed on mature Fagus sylvatica (n=3) and Pseudotsuga menziesii (n=3) individuals in a mixed temperate forest. We additionally measured sap flux density (Js), radial growth and tree water deficit (TWD) to gain a holistic picture of seasonal leaf and stem water and carbon flux dynamics during the summer of 2024. During midsummer, we found a gradual reduction of stomatal conductance (gs) and VOC emissions of sun, but not shade branchlets of P. menziesii in response to moderate atmospheric and edaphic drying. Decreased gs led to a downregulation of transpiration (E), Js, and carbon isotope discrimination accompanied by an increase in TWD and intrinsic water used efficiency. Leaf gas exchange of shade branchlets remained unaffected due to microclimatic buffering effects. Contrarily, sun leaves of F. sylvatica, profited from sunny midsummer conditions and increased leaf gas exchange, whereas shade leaves benefitted from more diffuse light during early summer exhibiting similar carbon assimilation, transpiration and VOC emissions as sun leaves. For both species we found a clear time lag of four to five hours between maximum leaf and stem water fluxes and a delay of up to 20 hours for the recovery of TWD, highlighting the role of stem water reserves. Pronounced seasonal and diurnal differences of leaf gas exchange, stem water fluxes and VOC emissions showed, that continuous data are essential to better understand variability of ecosystem flux dynamics.

plant biology↗

bioRxiv2025

Continuous chlorophyll fluorescence measurements trace sudden cold spell effects on photosynthetic efficiency in a temperate mixed forest

Air temperature extremes and fluctuations are expected to become more frequent and have been shown to affect the physiological functioning of temperate forests across Europe. The exposure to sudden cold spells during summer combined with high light intensities can lead to photoinhibition of photosystem II (PSII) and thereby reduce photosynthetic efficiency. In this study, we aimed to analyse the dynamics of photoinhibition as well as related protection mechanisms of tall tree canopies during such cold spells. Therefore, we continuously assessed leaf level chlorophyll fluorescence (ChlF) and ecosystem carbon fluxes by eddy covariance in a temperate mixed forest in southern Germany during growing season 2024. While the deciduous, broadleaved F. sylvatica indicated chronic photoinhibition as a response to sudden cold spells, the evergreen, coniferous P. menziesii showed higher tolerance to low air temperatures. Both species increased thermal energy dissipation at PSII during cold spells indicating the activation of protective mechanisms. Likewise, both species exhibited mainly the sustained form of non-photochemical quenching (NPQs) as a reaction to chilling temperatures and high light intensities which maintained elevated during the recovery phases. However, the dynamics of upregulation of photoprotection and recovery processes differed between the two tree species. Furthermore, the integration of ecosystem carbon exchange and continuous leaf level ChlF measurements gave valuable insights into the photosynthetic dynamics of the mixed forest canopy. This study emphasises the importance of the assessment of dynamic responses to future climate impacts on forest ecosystem on different scales.

ecology↗

bioRxiv2021

Imagined speech can be decoded from low- and cross-frequency features in perceptual space

Reconstructing intended speech from neural activity using brain-computer interfaces (BCIs) holds great promises for people with severe speech production deficits. While decoding overt speech has progressed, decoding imagined speech have met limited success, mainly because the associated neural signals are weak and variable hence difficult to decode by learning algorithms. Using three electrocorticography datasets totalizing 1444 electrodes from 13 patients who performed overt and imagined speech production tasks, and based on recent theories of speech neural processing, we extracted consistent and specific neural features usable for future BCIs, and assessed their performance to discriminate speech items in articulatory, phonetic, vocalic, and semantic representation spaces. While high-frequency activity provided the best signal for overt speech, both low- and higher-frequency power and local cross-frequency contributed to successful imagined speech decoding, in particular in phonetic and vocalic, i.e. perceptual, spaces. These findings demonstrate that low-frequency power and cross-frequency dynamics contain key information for imagined speech decoding, and that exploring perceptual spaces offers a promising avenue for future imagined speech BCIs.

neuroscience↗

bioRxiv2018

Coherence-based spectro-spatial fillters for stimulus features prediction from electrocorticographic recordings.

The traditional approach in neuroscience relies on encoding models where brain responses to different stimuli are related to the latter to establish reproducible dependencies. To reduce neuronal and experimental noise, brain signals are usually averaged across trials to detect reliable and coherent brain activity. However, neural representations of stimulus features can be spread over time, frequency, and space, motivating the use of alternative methods that relate stimulus features to brain responses. We propose a Coherence-based spectro-spatial filter method that reconstructs stimulus features from intracortical brain signals. The proposed method models trials of an experiment as realizations of a random process and extracts patterns that are common across brain signals and the presented stimuli. These patterns, originating from different recording sites, are then combined (spatial filtering) to form a final prediction. Our results from three different cognitive tasks (motor movements, speech perception and speech production), concur to show that the proposed method significantly improves the ability to predict stimulus features over traditional methods such as multilinear regression with distributed lags and artificial neural networks. Furthermore, analyses of the model parameters show anatomical discriminability for execution of different motor movements. This anatomical discriminability is also observed in the perception and production of different words. These features could be exploited in the design of neuroprosthesis, as well as for exploring normal brain functioning.

neuroscience↗