Simulated Landscape Genetic Data for Regression Method Comparison

Population genetic simulations evaluate seven regression-based model selection methods for landscape genetics. The study assesses method performance across varied landscapes differing in resistance variables, magnitude, and functional relationships. Andrew J. Shirk authored this dataset, which was last updated in June 2020.

Use Cases

• Compare the accuracy of Linear Mixed Effects Models against other methods like Mantel tests for landscape genetic inference.
• Assess model selection performance under conditions of high landscape resistance and limited hypothesis correlation.
• Evaluate the effect of transformations designed to linearize relationships between genetic and landscape distances.
• Analyze simulation outcomes across landscapes varying in the number and type of resistance variables.

Strengths

• Dataset is derived from a structured simulation study evaluating seven distinct regression-based model selection methods.
• The underlying research provides specific guidance on method performance under varied landscape resistance and correlation conditions.
• Data is associated with peer-reviewed research published in 2020.

Limitations

• The dataset consists of simulation results, not empirical genetic or landscape measurements from real populations.
• Specific details on data volume, such as the number of simulation runs or rows, are not provided in the input.
• The scope is limited to the seven regression methods and landscape scenarios described in the study.

Provenance

Source

Andrew J. Shirk via the Dryad repository.

Collection Method

Generated through population genetic simulations modeling various landscape resistance scenarios.

Freshness

Last updated on 2020-06-24.