Simulation Data for Evaluating Genetic Barrier Detection Methods

Comprising simulation results used to evaluate the performance of seven statistical methods for detecting linear barriers to gene flow. The study compared methods including Monmonier's algorithm, WOMBLING, TESS, GENELAND, STRUCTURE, PSMIX, and DAPC under varying conditions like dispersal ability and genetic equilibrium. It was authored by Christopher Blair and published in 2020.

Use Cases

• Compare the success rates of boundary-detection methods like Monmonier's algorithm and WOMBLING against clustering methods such as TESS and GENELAND.
• Analyze the effect of simulation conditions like dispersal ability on the time to barrier detection for all evaluated methods.
• Assess the potential for incorrect barrier inference by clustering methods like STRUCTURE, PSMIX, and DAPC under an isolation by distance model.

Strengths

• Evaluates seven distinct analytical methods for genetic barrier detection.
• Simulation conditions test method performance across factors like dispersal ability and genetic equilibrium.
• Published under a permissive CC0 1.0 license in 2020.

Limitations

• Data is simulated, not empirical, which may limit direct applicability to real-world populations.
• Specific dataset metrics like row count, column count, and file formats are unknown.
• The study's conclusions are specific to the detection of linear barriers, not other forms of population structure.

Provenance

Source

Christopher Blair via Dryad.

Collection Method

Data generated from simulations evaluating genetic barrier detection methods.

Freshness

Last updated in 2020.