Inferring coexistence likelihood in changing environments from ecological time series.

Area of Science:

• Ecology
• Population Dynamics
• Community Ecology

Background:

• Understanding species coexistence is crucial for predicting biodiversity patterns.
• Estimating ecological parameters like niche and fitness differences typically requires isolating organisms, which can alter their natural interactions.
• Environmental changes significantly influence species coexistence mechanisms.

Purpose of the Study:

• To develop a novel method for estimating key ecological parameters (per capita interactions, intrinsic growth rates) directly from time-series community data.
• To infer dynamic niche differences and species resistance that vary with time and environmental conditions.
• To provide a tool for analyzing ecological dynamics and community structures in changing environments.

Main Methods:

• Weighted multivariate regression applied to per capita growth rates from species-rich community time-series data.
• Inference of niche differences and species resistance metrics.
• Validation using synthetic data and application to experimental and observational datasets.

Main Results:

• Demonstrated an allocative trade-off between grazing resistance and growth rate in algae.
• Showed that stressful environmental conditions decrease coexistence likelihood and disturb community balance.
• Observed seasonal variations in autotrophic guild growth rates and interactions, potentially explaining cyanobacteria blooms.

Conclusions:

• The developed approach enables direct estimation of ecological parameters from community data, offering insights into species coexistence mechanisms.
• This method reveals how environmental variability affects community dynamics and stability.
• The findings contribute to understanding biodiversity patterns and addressing ecological challenges like harmful algal blooms.