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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.
Summary
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.
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.
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