Speaker
Description
Understanding how species respond to increasing frequency of extreme weather events is important for predicting biodiversity change in a rapidly shifting climate. While long-term climatic trends are known to drive gradual range shifts, short-term redistribution of species in response to abrupt extremes remains less well quantified. In the first phase of this project, a dynamic occupancy modeling framework was developed to explore species responses to isolated and hypothetical extreme weather scenarios, indicating that extreme events can alter short-term distribution patterns.
Building on these results, the model is applied to empirical weather time series data to examine how historical climatic variability relates to species occupancy through time. Using monthly weather data from 2000–2018 together with species occurrence records, past occupancy dynamics are reconstructed and the influence of temporal weather variability on the short-term distribution of 132 bird species of conservation interest in Central Europe (Germany, Austria, and Switzerland) is quantified.
Weather time series are incorporated as temporally explicit predictors within the dynamic occupancy model, allowing assessment of responses to extreme events as well as the occurrence of multiple extreme conditions within the same time period (“stacked” extremes). The analysis evaluates whether higher weather variability is associated with greater variability in occupancy patterns and whether stacked extreme conditions are associated with stronger deviations in occupancy.
This approach links climatic variability and extreme weather structure with observed species occupancy dynamics using a consistent modeling framework across simulated and empirical conditions.
| Status Group | Doctoral Researcher |
|---|---|
| FOR TALKS: Poster Presentation Option | Yes, I’m willing to present as a poster. |