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Inferring coexistence likelihood in changing environments from ecological time series.

Phuong L Nguyen1, Francesco Pomati2, Rudolf P Rohr1

  • 1Department of Biology, University of Fribourg, Fribourg CH-1700, Switzerland.

Proceedings of the National Academy of Sciences of the United States of America
|July 10, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces a new method to estimate species interactions and growth rates directly from community data, revealing how environmental changes impact species coexistence and community dynamics.

Keywords:
Lotka–Volterra mapcoexistence metricsintrinsic growth rateper capita interaction strengthtime-series data

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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.