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Catching ghosts with a coarse net: use and abuse of spatial sampling data in detecting synchronization
Natalia Petrovskaya1, Sergei Petrovskii2
1School of Mathematics, University of Birmingham, Birmingham B15 2TT, UK.
Coarse spatial sampling can distort population synchronization analysis. Estimation errors lead to inaccurate correlation coefficients, potentially showing false synchronization or masking true patterns.
Area of Science:
- Ecology
- Mathematical Biology
- Statistical Ecology
Background:
- Population dynamics synchronization across habitats is common.
- Correlation coefficients are used to detect synchronization from spatial sampling data.
- Coarse sampling grids introduce significant estimation errors.
Purpose of the Study:
- To investigate the impact of estimation errors from coarse spatial sampling on correlation coefficients.
- To demonstrate how sampling grid coarseness affects the accuracy of synchronization detection.
- To identify and address artifactual synchronization ('ghost synchronization').
Main Methods:
- Analysis of several population models.
- Calculation of correlation coefficients using simulated spatial sampling data.
- Evaluation of the influence of sampling grid resolution on correlation values.
Main Results:
- Estimation errors from coarse grids severely reduce correlation coefficient accuracy.
- Calculated correlations rarely exceed 0.5, even for highly synchronized populations.
- Observed 'ghost synchronization' where high correlations appear despite no true synchronization.
Conclusions:
- Coarse spatial sampling can render correlation-based synchronization analysis unreliable.
- Artifactual synchronization is a significant issue with insufficient sampling resolution.
- A simple test is proposed to assess grid coarseness and validate correlation results.
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