InfoMating

InfoMating analyzes non-random mating patterns using an information-theoretic framework to model mate competition, mate choice, sexual selection, and assortative mating for discrete traits.

Key Features:

• Modeling framework: Models discrete traits with any number of phenotypes and links causes (mate competition and mate choice) to consequences (sexual selection and assortative mating).
• Information-theoretic partition: Applies an informational partition of non-random mating effects to separate and quantify contributing processes.
• Mutual propensity parameters: Represents mate choice and mate competition through mutual propensity parameters.
• Maximum likelihood estimates: Derives formulas for maximum likelihood estimates to enable parameter estimation within each model.
• Multi-model inference: Uses information criteria to perform model selection and multi-model inference among candidate models.
• Simulation and validation: Employs simulation to evaluate performance in model selection and parameter estimation.
• Empirical application: Applied to ecotype data of the marine gastropod Littorina saxatilis from Galicia (Spain), yielding models with both mate choice and competition that produced positive assortative mating and female sexual selection.
• Standardized methodology: Provides a standardized approach for model selection and multi-model inference of mating parameters for discrete traits.

Scientific Applications:

• Understanding evolutionary processes: Distinguishes among processes underlying observed mating patterns to infer mechanisms of sexual selection and assortative mating.
• Empirical studies: Connects empirical mating data with theoretical models, exemplified by analyses of Littorina saxatilis ecotypes from Galicia (Spain).

Methodology:

Uses an informational partition of non-random mating effects; models mate choice and competition via mutual propensity parameters; derives maximum likelihood estimate formulas; applies information criteria for multi-model inference; and validates performance through simulation.