Data and models from multi-model inference of non-random mating from an information theoretic approach

Area of Science:

• Evolutionary Biology
• Behavioral Ecology
• Quantitative Genetics

Background:

• Understanding mating patterns is crucial for evolutionary and behavioral ecology.
• Assessing sexual selection and non-random mating requires robust statistical methodologies.
• Simulated datasets are valuable for testing and developing these methodologies.

Purpose of the Study:

• To generate a comprehensive simulated dataset for analyzing mating patterns.
• To provide data for testing the performance of multi-model inference for non-random mating.
• To offer a resource for evaluating statistics related to sexual selection and assortative mating.

Main Methods:

• Simulated mating data using the MateSim program.
• Modeled one-sex competition and mate choice scenarios.
• Generated datasets for large and small populations, with and without replacement sampling.

Main Results:

• Created simulated datasets representing random mating, female competition, and male competition with mate choice.
• Simulated 1000 replicates for each of five different mating model cases.
• Included datasets for monogamous species with varying population sizes and sampling methods.

Conclusions:

• The generated data is suitable for testing multi-model inference of non-random mating.
• This dataset can be used to evaluate existing and novel statistics for mating pattern analysis.
• The data serves as a valuable resource for research in sexual selection and assortative mating.