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Local-manifold-distance-based regression: an estimation method for quantifying dynamic biological interactions with

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A new method, local manifold distance-based regression (LMDr), improves ecological interaction analysis. LMDr enhances prediction accuracy and robustness to noise in complex ecosystems, offering better insights into species dynamics.

Keywords:
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Area of Science:

  • Ecology
  • Complex Systems Analysis
  • Time Series Analysis

Background:

  • Quantifying species interactions is vital for ecological understanding.
  • Nonlinear time-series analyses like S-maps show promise for ecosystem studies.
  • Existing methods face limitations in Jacobian estimation accuracy with noisy, non-equilibrium data.

Purpose of the Study:

  • To introduce a novel, robust analytical method for ecological interaction quantification.
  • To address limitations in Jacobian estimation accuracy in nonlinear time-series analyses.
  • To improve prediction and interaction effect estimation in complex ecosystems.

Main Methods:

  • Introduced the local manifold distance (LMD) concept, a non-equidistant measure.
  • Developed LMD-based regression (LMDr) by integrating LMD with advanced computation.
  • Validated LMDr using synthetic time series and an experimental protozoan predator-prey system.

Main Results:

  • LMDr demonstrated superior robustness to noise compared to existing methods.
  • The method showed improved accuracy in Jacobian estimation for ecological interactions.
  • Application to a protozoan system revealed better correspondence with known predator-prey dynamics.

Conclusions:

  • LMDr offers a robust and efficient approach for analyzing complex ecological networks.
  • The method enhances the understanding of species interactions under dynamic and noisy conditions.
  • LMDr advances ecological time-series analysis for prediction and interaction quantification.