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TESTING INFERENCES ABOUT MICRO-EVOLUTIONARY PROCESSES BY MEANS OF SPATIAL AUTOCORRELATION ANALYSIS.

Robert R Sokal1, Geoffrey M Jacquez1

  • 1Department of Ecology and Evolution, State University of New York at Stony Brook, Stony Brook, NY, 11794-5245, USA.

Evolution; International Journal of Organic Evolution
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Summary
This summary is machine-generated.

Spatial autocorrelation analysis accurately infers microevolutionary processes in simulated populations. This method, using spatial correlograms, is more sensitive than visual inspection for detecting trends in gene frequencies.

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Area of Science:

  • Population Genetics
  • Spatial Analysis
  • Evolutionary Biology

Background:

  • Understanding microevolutionary processes is crucial for population genetics.
  • Spatial autocorrelation analysis offers a quantitative method to study population structures.
  • Simulations allow for controlled investigation of evolutionary forces.

Purpose of the Study:

  • To evaluate the efficacy of spatial autocorrelation analysis in inferring microevolutionary processes.
  • To determine the accuracy of spatial correlograms in detecting isolation by distance, migration, and selection.
  • To compare the sensitivity of spatial autocorrelation analysis with visual inspection of gene-frequency surfaces.

Main Methods:

  • Generation of simulated gene-frequency surfaces under isolation by distance, migration, and selection.
  • Assembly of six datasets with 12-15 independent allele-frequency surfaces to mimic population samples.
  • Application of spatial autocorrelation analysis, including spatial correlograms and clustering, to simulated data.

Main Results:

  • Spatial autocorrelation analysis correctly inferred microevolutionary processes in five out of six simulated datasets.
  • Inference errors occurred with weak migration, weak selection on complex backgrounds, or isolation by distance alone.
  • Spatial correlograms demonstrated higher sensitivity in detecting trends compared to visual inspection.

Conclusions:

  • Spatial autocorrelation analysis is a reliable tool for detecting microevolutionary processes in natural populations.
  • Joint interpretation of correlograms and their clusters enhances inference accuracy.
  • Utilizing a large number of gene frequencies across multiple loci improves the robustness of the analysis.