Speaker
Description
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 novel methodological approach using nonlinear dimension reduction, which effectively extracts spatio-temporal ecological gradients from large and diverse datasets.
Our approach quantifies synchronised behaviour across thousands of plant occurrence observations, revealing how plants respond to intra-annual variablity at macroecological scales. We demonstrate our approach using datasets collected by citizen scientists via the Flora Incognita plant identification app (10 million observations in Germany, 2018-2021). This analysis shows that synchronised group behaviour during the growing season is compressible into a few ecological gradients, while synchrony deteriorates outside the growing season.
We propose linking these findings to other observations such as satellite or botanical garden observations. This framework advances plant macrophenology, providing researchers with practical tools to quantify climate change effects on plant life cycles.
https://besjournals.onlinelibrary.wiley.com/doi/full/10.1111/2041-210X.14365
| Status Group | Postdoctoral Researcher |
|---|---|
| FOR TALKS: Poster Presentation Option | No, I prefer to present only as a talk. |