- 1Leipzig University, Institute for Earth System Science and Remote Sensing, Leipzig, Germany (karin.mora@uni-leipzig.de)
- 2Max Planck Institute for Biogeochemistry, Jena, Germany
- 3German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig, 04103 Leipzig, Germany
- 4Department for Sensor-based Geoinformatics, University of Freiburg, 79106 Freiburg, Germany
- 5Data Intensive Systems and Visualisation, Technische Universität Ilmenau, 98693 Ilmenau, Germany
- 6Faculty of Biological Sciences, Friedrich Schiller University, 07743 Jena, Gemany
- 7Center for Scalable Data Analytics and Artificial Intelligence, ScaDS.AI Dresden/Leipzig, Germany
Phenological changes are key indicators of climate change. While most studies focus on individual species, plant macrophenology examines large-scale patterns and processes in the timing of plant life cycle events, such as flowering, across extensive spatial and temporal scales. Traditional methods often struggle to capture the complexity of these patterns. To address this, we developed a pioneering methodological approach using nonlinear dimension reduction [1], which effectively extracts spatio-temporal patterns from large and diverse phenological datasets.
Our approach reveals ecological gradients that capture underlying structures and relationships missed by linear methods [1,2]. A primary objective is to quantify synchronised behaviour across thousands of plant species, offering insights into the collective responses of plant communities to climate variability and change. By identifying and analysing synchronisation patterns, we aim to detect shifts in plant phenology and understand their broader ecological impacts
We demonstrate the versatility of our approach by applying it to datasets collected by citizen scientists using mobile applications such as Flora Incognita [3], a plant identification app. Additionally, we explore phenological changes across annual cycles and propose linking these findings to large-scale measurements such as eddy covariance and satellite data.
Incorporating citizen science datasets enhances the resolution and accuracy of our analyses, enabling robust conclusions about the impact of climate variability on plant phenology. This framework advances plant macrophenology, providing researchers with practical tools to quantify and monitor climate change effects on plant life cycles.
[1] Mora et al. (2024) Methods Ecol Evol, http://doi.org/10.1111/2041-210X.14365
[2] Mahecha et al. (2021) Ecography, 44: 1131-1142 https://doi.org/10.1111/ecog.05492
[3] Mäder et al. (2021) Methods Ecol Evol, 12: 1335-1342 https://doi.org/10.1111/2041-210X.13611
How to cite: Mora, K., Rzanny, M., Wäldchen, J., Feilhauer, H., Guimarães-Steinicke, C., Kattenborn, T., Kraemer, G., Mäder, P., Svidzinska, D., Wieneke, S., Wolf, S., and Mahecha, M. D.: Plant macrophenological dynamics - variations in plant group behaviour revealed by citizen science data, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-16628, https://doi.org/10.5194/egusphere-egu25-16628, 2025.
