Bayes Factor Species Delimitation Simulations and Sceloporus scalaris Data

This dataset contains simulation results and empirical genetic data from the Sceloporus scalaris lizard species group, used to evaluate Bayes factor methods for species delimitation. It was created by Jared A. Grummer and published in 2020. The data supports the recognition of all scalaris group taxa and three previously undescribed lineages as independent evolutionary lineages.

Use Cases

• Compare marginal likelihood estimates from harmonic mean, smoothed harmonic mean, path-sampling, and stepping-stone methods for species delimitation model selection.
• Test the performance of Bayes factor species delimitation when reassigning individuals between species versus lumping or splitting lineages.
• Validate species delimitation results by comparing Bayes factor outcomes with those from the reversible-jump MCMC (rjMCMC) method implemented in BP&P.
• Assess the repeatability and consistency of model rankings produced by the Akaike information criterion through Markov chain Monte Carlo analysis (AICM) on simulated and empirical datasets.

Strengths

• Data is derived from a published scientific study on species delimitation, providing empirical validation.
• Includes results from four distinct marginal likelihood estimation methods (harmonic mean, smoothed harmonic mean, path-sampling, stepping-stone) for comparison.
• Empirical data focuses on the Sceloporus scalaris species group, a specific taxonomic group in Squamata.

Limitations

• The specific number of rows, columns, and file size are unknown, limiting assessment of dataset scale.
• Sample data and file formats are unavailable, making initial inspection of data structure impossible.
• The dataset is static with a last update in 2020, potentially lacking newer methodological developments.

Provenance

Source

Dryad digital repository.

Collection Method

Generated through simulations and analysis of empirical genetic data from the Sceloporus scalaris species group.

Freshness

Last updated on 2020-06-24.