Biology subjects

Kattenborn, T.

Publications and source records attributed to Kattenborn, T..

6 recordsLinked to original sources

bioRxiv2026

Global Convergence of Plant Functional Trait Composition in the Anthropocene

Since the onset of European colonial expansion, humans have accelerated species migration across continents, reshaping plant functional composition and associated ecosystem processes. Plant functional traits-such as leaf area, plant height, or rooting depth-are structured along major axes of variation, including size and leaf economics, that reflect ecological strategies. While human-mediated changes in this trait space have been documented regionally or for specific taxa, there exists no global, grid cell-level quantification of past shifts across major axes of trait variation. Here, we link global citizen science plant occurrence data with data on 37 above- and below-ground traits, and information on native and introduced status for each occurrence. Using dimension-reduction on grid cell-level trait means and introduced species status as a proxy for anthropogenic change, we identify three major axes of functional variation: the size, leaf economics, and life-span axes. By comparing past (native-only) and present-day trait distributions in 3D trait space and geographically, we find prominent region-specific shifts along all three axes. Overall, functional composition converges toward (mostly) smaller, more acquisitive, and shorter-lived assemblages, with region-specific differences in which axis shifts are most pronounced. These results provide the first global estimate of how human-mediated plant introductions have altered ecosystem functional composition in the past centuries, highlighting the spatial patterns and trait dimensions most affected by anthropogenic pressures.

ecology↗

bioRxiv2026

Controls of spatio-temporal patterns of soil respiration in a mixed forest

IntroductionPatterns of soil respiration (Rs) are heterogeneous on spatio-temporal scales. The best studied controlling factors of Rs are microclimatic conditions such as soil temperature and moisture. However, a strong pronounced seasonality shifts Rs patterns from temperature to moisture-controlled regimes. Hereby, lag effects and influences of trees in mixed forests on large spatio-temporal scales are rarely investigated. Material and MethodsWe investigated Rs over two years on a weekly to fortnightly measurement frequency at a 1 ha area in a mixed forest on 35 predefined locations using open bottom chambers. Analysis was derived using downscaled meteorological data and a tree species map. ResultsOur data confirmed soil temperature and moisture as important variables which explained up to 72% in variability. Yet, our results revealed an additional predictor: the atmospheric vapour pressure. Together with seasonality and the vapour pressure deficit, 84% in variability could be explained. Additionally, the predictors traced a decrease in Rs due to drought and an increase due to rewetting. By tendency, Rs correlated negatively with distance to the tree and Rs was significantly higher in broadleaf patches compared to coniferous and mixed patches during the summer seasons. ConclusionWe conclude that meteorological conditions might serve as valuable predictors for CO2 emissions from forest soil. This enables upscaling of Rs given the dense availability of meteorological data. Tree species distribution partly explained the spatial patterns of Rs. However, a detailed analysis of local soil properties would enhance our understanding of the interactions between soils, plants and the atmosphere.

ecology↗

bioRxiv2025

AngleCam V2: Predicting leaf inclination angles across taxa from daytime and nighttime photos

O_LIUnderstanding how plants capture light and maintain their energy balance is crucial for predicting how ecosystems respond to environmental changes. By monitoring leaf inclination angle distributions (LIADs), we can gain insights into plant behaviour that directly influences ecosystem functioning. LIADs affect radiative transfer processes and reflectance signals, which are essential components of satellite-based vegetation monitoring. Despite their importance, scalable methods for continuously observing these dynamics across different plant species throughout day-night cycles are limited. C_LIO_LIWe present AngleCam V2, a deep learning model that estimates LIADs from both RGB and near-infrared (NIR) night-vision imagery. We compiled a dataset of over 4,500 images across 200 globally distributed species to facilitate generalization across taxa. Moreover, we developed a method to simulate pseudo-NIR imagery from RGB imagery to enable an efficient training of a deep learning model for tracking LIADs across day and night. The model is based on a vision transformer architecture with mixed-modality training using the RGB and the synthetic NIR images. C_LIO_LIAngleCam V2 achieved substantial improvements in generalization compared to AngleCam V1 (R2 = 0.62 vs 0.12 on the same holdout dataset). Phylogenetic analysis across 100 genera revealed no systematic taxonomic bias in prediction errors. Testing against leaf angle dynamics obtained from multitemporal terrestrial laser scanning demonstrated the reliable tracking of diurnal leaf movements (R2 = 0.61-0.75) and the successful detection of water limitation-induced changes over a 14-day monitoring period. C_LIO_LIThis method enables continuous monitoring of leaf angle dynamics using conventional cameras, enabling applications in ecosystem monitoring networks, plant stress detection, interpreting satellite vegetation signals, and citizen science platforms for global-scale understanding of plant structural responses. C_LI

ecology↗

bioRxiv2025

Prediction in trait-based ecology: global simulations of specific leaf area using a trait-based dynamic vegetation model

AbstractPredicting plant community functional traits is considered a Holy Grail of trait-based ecology because traits underpin ecosystem processes. Previous statistical, machine learning, and optimality approaches have produced global plant trait predictions. However, the utility of trait-based vegetation models, which include demographic processes and can represent trait diversity, remains unexplored at this scale. We use aDGVM2-LL, a trait- and individual-based dynamic global vegetation model (DGVM). aDGVM2-LL simulates community assembly, which is driven by natural selection, biotic, and abiotic conditions; simulated specific leaf area (SLA) is an emergent outcome of community assembly. We examine: 1) how well aDGVM2-LL can simulate global SLA by examining deviations from trait data, and 2) explore drivers of strong deviations. Compared to GBIF-derived SLA data, aDGVM2-LL displays mean SLA differences of -2.9 (m2/kg)(GBIF range ca. 4 - 35 m2/kg), a root mean square error (RMSE) of 7.25, and normalised mean absolute error (nMAE) of 26.54%. Published statistical, machine learning, and optimality approaches displayed differences with GBIF-derived trait data which range between (mean : -4.83 - 2.67, RMSE: 4.41 - 6.68, nMAE: 13.41% - 25.20%). Thus, aDGVM2-LL mean differences are comparable with published predictions while RMSEs and nMAEs are higher. Large aDGVM2-LL mismatches occur in areas where the model incorrectly simulates the relative abundances of deciduous vs. evergreen leaf phenologies. Correcting mismatches in leaf phenological abundances strongly reduces the range of mean SLA differences (-0.14 - 0.43), RMSEs (5.85 - 5.90), and nMAEs (15.44% - 20.61%). These results show that an eco-evolutionary, process-based approach can reasonably simulate global SLA values, particularly when leaf phenological abundances are accurate. Our results highlight the general importance of the global drivers of leaf phenology for leaf traits. The correct simulation of the relative abundances of deciduous and evergreen leaf phenologies is crucial to predict contemporary and future SLA.

ecology↗

bioRxiv2024

Large-scale remote sensing reveals that tree mortality in Germany appears to be greater than previously expected

Global warming poses a major threat to forests and events of increased tree mortality are observed globally. Studying tree mortality often relies on local-level observations of dieback while large-scale analyses are lacking. Satellite remote sensing provides the spatial coverage and sufficiently high temporal and spatial resolution needed to investigate tree mortality at landscape-scale. However, adequate reference data for training satellite-based models are scarce. In this study, we employed the first maps of standing deadwood in Germany for the years 2018-2022 with 10 m spatial resolution that were created by using tree mortality observations spotted in hundreds of drone images as the reference. We use these maps to study spatial and temporal patterns of tree mortality in Germany and analyse their biotic and abiotic environmental drivers using random forest regression. In 2019, the second consecutive hotter drought year in a row, standing deadwood increased steeply to 334 {+/-} 189 kilohectar (kha) which corresponds to 2.5 {+/-} 1.4% of the total forested area in Germany. Picea abies, Pinus sylvestris, and Fagus sylvatica showed highest shares of standing deadwood. During 2018-2021 978 {+/-} 529 kha (7.9 {+/-} 4.4%) of standing dead trees accumulated. The higher mortality estimates that we report compared to other surveys (such as the ground-based forest condition survey) can be partially attributed to the fact that remote sensing captures mortality from a birds eye perspective and that the high spatial detail (10 m) in this study also captures scattered occurrences of tree mortality. Atmospheric drought (i.e., climatic water balance and vapor pressure deficit) and temperature extremes (i.e., number of hot days and frosts after vegetation onset) were the most important predictors of tree mortality. We found increased tree mortality for smaller and younger stands and also on less productive sites. Monospecific stands were generally not more affected by mortality, but only when interactions with damaging insects (e.g., bark beetles) occurred. Because excess tree mortality rates threaten many forests across the globe, similar analyses of tree mortality are warranted and technically feasible at the global scale. We encourage the international scientific community to share and compile local data on deadwood occurrences (see example: www.deadtrees.earth) as such a collaborative effort is required to help understand mortality events on a global scale.

ecology↗

bioRxiv2024

Forest dieback in drinking water protection areas - a hidden threat to water quality

For centuries, forests have been considered a natural safeguard for drinking water quality. We challenge this view in light of the rising frequency of climate extremes, such as droughts coinciding with high temperatures, which have caused unprecedented pulses of forest dieback globally. Drought-induced forest diebacks may jeopardize the crucial role of forests in protecting water quality, potentially even turning forests into sources of contamination. To underscore the critical importance of the topic, here we provide the first comprehensive assessment of forest cover, type, and dieback (assessed as canopy cover loss) across drinking Water Protection Areas (WPAs) in Germany, one of the countries hit most severely by the unprecedented Central European drought of 2018-2020. Our findings reveal a high forest cover of 43% in WPAs, from which a substantial amount of 5% canopy cover got lost within only three years as a direct or indirect consequence of the drought. Spruce-dominated forests, constituting 28% of all forests in WPAs, were particularly susceptible, but other dominant tree species also experienced anomalously high mortality rates. Combining this assessment with exemplary records of nitrate concentrations in the groundwater of WPAs revealed that forest dieback can significantly impair drinking quality. On average, nitrate concentrations more than doubled in WPAs with severe forest dieback, whereas nitrate concentrations did not significantly change in undisturbed WPAs. However, we also found pronounced differences between WPAs affected by forest dieback, underlining the need for further data and research to derive a generalizable understanding of the underlying mechanisms and controls. Based on this assessment, we deduce critical data and knowledge gaps essential to developing well-informed prediction, adaptation, and mitigation strategies. We call for interdisciplinary research addressing the hidden threat forest dieback poses for our drinking water resources.

biochemistry↗