Resource limitation underlying multiple masting models makes mast seeding sensitive to future climate change

Area of Science:

• Ecology
• Plant Sciences
• Mathematical Modeling

Background:

• Mast seeding, or the synchronized mass flowering in plants, is a complex phenomenon.
• Understanding the drivers of mast seeding is crucial for predicting plant responses to environmental change.
• Previous studies have used empirical models, but mechanistic understanding remains debated.

Purpose of the Study:

• To develop and validate a novel mechanistic resource-based model for mast seeding.
• To compare the performance of mechanistic and empirical models in explaining mast seeding patterns.
• To investigate the influence of site fertility and climate on mast seeding drivers.

Main Methods:

• Developed a new mechanistic resource-based model for mast seeding.
• Validated the model using four 40-year Chionochloa (snow tussock) datasets.
• Compared mechanistic model predictions with empirical statistical models using simulated and observed data.

Main Results:

• The mechanistic model explained 90-99% of variation in Chionochloa flowering.
• Lower fertility sites showed higher resource mobilization and lower probability of climatic induction.
• Empirical models incorporating resource-limited induction mechanisms outperformed alternatives.

Conclusions:

• Resource-limited floral induction is supported as a key mechanism in mast seeding.
• Climate change is expected to impact mast seeding patterns due to climate sensitivity of resource acquisition and flowering cues.
• Mechanistic models provide robust insights into plant reproductive strategies under environmental change.