Biology subjects

Brandl, S.

Publications and source records attributed to Brandl, S..

3 recordsLinked to original sources

bioRxiv2022

Drought effects of annual and long-term temperature and precipitation on mortality risk for 9 common European tree species

Risk factors for natural tree mortality in managed forests, excluding wind and snow induced breakage, fires and thinning, can be difficult to identify due to correlated confounders of long- and short-term weather patterns with tree age. This study quantified the association of annual and long-term 30-year average temperature and precipitation effects on individual tree death across Europe from 2011 to 2020 for European beech, sessile and pedunculate oak, silver birch, black pine, Austrian oak, Scots pine, European hornbeam, and Norway spruce. For each species, logistic regression approaches for predicting annual mortality evaluated the influence of age, exposition and weather effects on individual tree death, while accounting for multi-collinearity of risk factors. For all species except sessile oak, higher 30-year-temperature averages were associated with higher odds of tree mortality. Effect size of other risk factors varied among species, with similar weather associations between Austrian and sessile oak on the one hand, and Scots pine, Norway spruce and pedunculate oak on the other hand. In particular, warmer winters reduced mortality for silver birch, sessile and Austrian oaks, while having the opposite association for the other species. Sessile oak was most robust against drought effects and could serve as an important tree species under climate change scenarios.

plant biology↗

bioRxiv2021

Fourier SPoC: A costumised machine-learning analysis pipeline for auditory beat-based entrainment in the MEG

We propose here (the informed use) of a customised, data-driven machine-learning pipeline to analyse magnetoencephalography (MEG) in a theoretical source space, with respect to the processing of a regular beat. This hypothesis- and data-driven analysis pipeline allows us to extract the maximally relevant components in MEG source-space, with respect to the oscillatory power in the frequency band of interest and, most importantly, the beat-related modulation of that power. Our pipeline combines Spatio-Spectral Decomposition as a first step to seek activity in the frequency band of interest (SSD, [1]) with a Source Power Co-modulation analysis (SPoC; [2]), which extracts those components that maximally entrain their activity with the given target function, that is here with the periodicity of the beat in the frequency domain (hence, f-SPoC). MEG data (102 magnetometers) from 28 participants passively listening to a 5-min long regular tone sequence with a 400 ms beat period (the "target function" for SPoC) were segmented into epochs of two beat periods each to guarantee a sufficiently long time window. As a comparison pipeline to SSD and f-SpoC, we carried out a state-of-the-art cluster-based permutation analysis (CBPA, [3]). The time-frequency analysis (TFA) of the extracted activity showed clear regular patterns of periodically occurring peaks and troughs across the alpha and beta band (8-20 Hz) in the f-SPoC but not in the CBPA results, and both the depth and the specificity of modulation to the beat frequency yielded a significant advantage. Future applications of this pipeline will address target the relevance to behaviour and inform analogous analyses in the EEG, in order to finally work toward addressing dysfunctions in beat-based timing and their consequences. Author summaryWhen listening to a regular beat, oscillations in the brain have been shown to synchronise with the frequency of that given beat. This phenomenon is called entrainment and has in previous brain-imaging studies been shown in the form of one peak and trough per beat cycle in a range of frequency bands within 15-25 Hz (beta band). Using machine-learning techniques, we designed an analysis pipeline based on Source-Power Co-Modulation (SPoC) that enables us to extract spatial components in MEG recordings that show these synchronisation effects very clearly especially across 8-20 Hz. This approach requires no anatomical knowledge of the individual or even the average brain, it is purely data driven and can be applied in a hypothesis-driven fashion with respect to the "function" that we expect the brain to entrain with and the frequency band within which we expect to see this entrainment. We here apply our customised pipeline using "f-SPoC" to MEG recordings from 28 participants passively listening to a 5-min long tone sequence with a regular 2.5 Hz beat. In comparison to a cluster-based permutation analysis (CBPA) which finds sensors that show statistically significant power modulations across participants, our individually extracted f-SPoC components find a much stronger and clearer pattern of peaks and troughs within one beat cycle. In future work, this pipeline can be implemented to tackle more complex "target functions" like speech and music, and might pave the way toward rhythm-based rehabilitation strategies.

neuroscience↗

bioRxiv2020

Global gut content data synthesis and phylogeny delineate reef fish trophic guilds

The diversity of life on our planet has produced a remarkable variety of biological traits that characterize different species. Such traits are widely employed instead of taxonomy to increase our understanding of biodiversity and ecosystem functioning. However, for species trophic niches, one of the most critical aspects of organismal ecology, a paucity of empirical information has led to inconsistent definitions of trophic guilds based on expert opinion. Using coral reef fishes as a model, we show that experts often disagree on the assignment of trophic guilds for the same species. Even when broad categories are assigned, 60% of the evaluated trait schemes disagree on the attribution of trophic categories for at least 20% of the species. This disagreement greatly hampers comparability across studies. Here, we introduce a quantitative, unbiased, and fully reproducible framework to define species trophic guilds based on empirical data. First, we synthesize data from community-wide visual gut content analysis of tropical coral reef fishes, resulting in trophic information from 13,961 individuals belonging to 615 reef fish species across all ocean basins. We then use network analysis to cluster the resulting global bipartite food web into distinct trophic guilds, resulting in eight trophic guilds, and employ a Bayesian phylogenetic model to predict trophic guilds based on phylogeny and maximum body size. Our model achieved a misclassification error of 5%, indicating that our approach results in a quantitative and reproducible trophic categorization scheme, which can be updated as new information becomes available. Although our case study is for reef fishes, the most diverse vertebrate consumer group, our approach can be applied to other organismal groups to advance reproducibility in trait-based ecology. As such, our work provides an empirical and conceptual advancement for trait-based ecology and a viable approach to monitor ecosystem functioning in our changing world.

ecology↗