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Quantifying and exploring state-dependent ecological interactions from time series data using Gaussian process

Taiju Yukihira1, Yutaka Osada2, Michio Kondoh1

  • 1Tohoku University Graduate School of Life Sciences, Sendai, Miyagi Prefecture, Japan.

Journal of the Royal Society, Interface
|July 8, 2025
PubMed
Summary

We developed a new Gaussian process regression method to accurately measure how ecological interactions change over time and with community states. This tool improves ecological predictions by reliably quantifying state-dependent species interactions.

Keywords:
Bayesian non-parametric inferenceJacobian matrixcommunity matrixnonlinearityspecies interactionstime-series analysis

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Area of Science:

  • Ecology
  • Ecological Modeling
  • Time Series Analysis

Background:

  • Ecological interactions are often nonlinear and change with community states.
  • Accurate inference of these state-dependent interactions is vital for ecological studies.
  • Existing methods may lack accuracy with noisy data or struggle to quantify state dependence.

Purpose of the Study:

  • To introduce a novel non-parametric inference method for quantifying state-dependent ecological interactions.
  • To extend the Gaussian process empirical dynamic modeling (GP-EDM) approach for nonlinear time series data.
  • To provide a reliable tool for analyzing dynamic ecological communities.

Main Methods:

  • Utilized Gaussian process regression for non-parametric inference.
  • Extended the Gaussian process empirical dynamic modeling (GP-EDM) framework.
  • Validated the method using synthetic and real-world nonlinear time series data.

Main Results:

  • The proposed method demonstrated higher inference accuracy, especially for noisy time series data, compared to S-map and regularized S-map.
  • The method analytically accounts for the dependence of interaction strengths on community states.
  • Enabled local evaluation of state-dependent interaction changes and provided reliable inference with uncertainty quantification (credible intervals).

Conclusions:

  • The novel Gaussian process regression method offers a robust approach for inferring state-dependent ecological interactions.
  • It provides enhanced accuracy and reliability in analyzing complex, dynamic ecological systems.
  • This method serves as a foundation for future research on state dependence in species interactions.