Individual-Based Model Data on Stabilizing Selection and Stasis

Featuring simulation data from an individual-based model investigating the detection of stabilizing selection in natural populations. It was created by Benjamin C. Haller in 2020 to test hypotheses explaining the paradox of stasis. The data supports the model's conclusion that stabilizing selection is difficult to detect empirically after populations adapt to a fitness peak.

Use Cases

• Analyze the relationship between trait variation and fitness peak breadth to understand detection limits of stabilizing selection.
• Investigate how non-selective deaths and population wandering around a fitness peak mask observable selection gradients.
• Model the impact of negative frequency-dependent selection on flattening fitness landscapes, termed 'squashed stabilizing selection'.
• Examine the effect of sample size on the empirical detection of stabilizing selection in simulated populations.

Strengths

• Data is derived from a published model testing a specific evolutionary hypothesis, providing a clear theoretical context.
• The dataset is associated with research addressing the well-known 'paradox of stasis' in evolutionary biology.
• It is openly licensed under CC0-1.0, allowing for unrestricted reuse and analysis.

Limitations

• The dataset consists of simulation outputs, not empirical field data, limiting direct applicability to real-world populations.
• Specific structural details like row count, column names, and file formats are unknown, complicating initial assessment.
• The data's last update was in June 2020, and its update frequency is unspecified.

Provenance

Source

Dryad digital repository.

Collection Method

Generated from an individual-based computational model simulating population evolution under an invariant stabilizing fitness function.

Freshness

Last updated on 2020-06-24.