Modelling asynchrony in phenology considering a dynamic representation of meteorological variables

Area of Science:

• Ecology and environmental science
• Climate change research
• Biodiversity studies

Background:

• Climate change is altering phenological events globally.
• Phenological mismatches between species can negatively impact biodiversity and ecosystem stability.
• Understanding species interactions within trophic networks is crucial for predicting ecological responses to climate change.

Purpose of the Study:

• To develop novel interaction indices quantifying synchrony and asynchrony among species across trophic levels.
• To incorporate dynamic meteorological data into phenological interaction analyses.
• To identify critical time windows influencing phenological events.

Main Methods:

• Utilized citizen science observations and remote sensing (Landsat) for phenological indicators (insect flight, vegetation green-up, bird arrival).
• Developed and applied phenological indices of synchrony-asynchrony.
• Implemented relative sliding time window analysis and stepwise regression to identify critical meteorological time windows.

Main Results:

• Identified several instances of phenological asynchrony within trophic levels.
• Quantified the level of synchrony and asynchrony among species groups across three trophic levels.
• Demonstrated the utility of a novel index incorporating meteorological data.

Conclusions:

• The developed interaction indices provide a new tool for studying species synchrony and interaction networks.
• Incorporating meteorological data enhances the understanding of phenological shifts and their ecological consequences.
• This approach opens new avenues for ecological research on climate change impacts.