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Updated: Oct 8, 2025

Genetic Mapping of Thermotolerance Differences Between Species of Saccharomyces Yeast via Genome-Wide Reciprocal Hemizygosity Analysis
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[Convergent cross mapping method and its application in ecology].

Dan-Yu Wang1, Yuan-Jun Zhu1, Xiao-Hui Yang1

  • 1Institute of Desertification Studies, Chinese Academy of Forestry, Beijing 100091, China.

Ying Yong Sheng Tai Xue Bao = the Journal of Applied Ecology
|December 24, 2021
PubMed
Summary

Convergent cross mapping (CCM) analyzes nonlinear time series causality by reconstructing state spaces. This method offers advantages over traditional tests for complex ecological systems, especially when accounting for spatial correlations.

Keywords:
Granger causality testcausalityconvergent cross mapping (CCM)nonlinear complex ecosystem

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Area of Science:

  • Nonlinear dynamics
  • Time series analysis
  • Ecological modeling

Background:

  • Traditional linear methods struggle with complex nonlinear systems.
  • Granger causality tests have limitations in detecting weak causal links.

Purpose of the Study:

  • Introduce Convergent Cross Mapping (CCM) for nonlinear causality analysis.
  • Highlight CCM's advantages over traditional methods.
  • Provide a guide to CCM's principles and implementation.

Main Methods:

  • State space reconstruction from time series data.
  • Convergent Cross Mapping (CCM) algorithm.
  • Consideration of spatial autocorrelation for multi-point analysis.

Main Results:

  • CCM effectively identifies causality in weakly to moderately coupled nonlinear multivariable systems.
  • CCM demonstrates superior performance compared to traditional Granger causality tests.
  • Accurate CCM application requires addressing spatial autocorrelation in multi-point time series.

Conclusions:

  • CCM is a powerful tool for uncovering complex causal relationships in nonlinear ecological systems.
  • Proper handling of spatial data is crucial for reliable CCM results.
  • CCM advances the analysis of causality in complex, real-world systems.