Blouch

Blouch implements Bayesian linear Ornstein-Uhlenbeck models for comparative hypotheses, enabling estimation of adaptation and phylogenetic inertia in continuous trait evolution.

Key Features:

• Bayesian Framework: Fits allometric and adaptive models in a Bayesian framework that allows incorporation of prior biological knowledge and biologically meaningful priors.
• Measurement Error Incorporation: Accounts for measurement error in trait data within all model implementations.
• Varying Effects: Supports varying intercepts and slopes across groups or predictors.
• Multilevel Modeling: Enables hierarchical modeling structures for complex comparative datasets.
• Fixed and Continuous Predictors: Accommodates both fixed adaptive optima (regimes or niches) and continuously changing optima influenced by other traits.
• Model Validation: Validated on simulated data showing good performance in recovering true evolutionary parameters for all models.

Scientific Applications:

• Adaptation-Inertia Framework: Estimates effects of both adaptation and phylogenetic inertia on trait evolution.
• Allometric Modeling: Tests hypotheses about scaling relationships between traits, such as antler size to body size in deer.
• Adaptive Hypothesis Testing: Evaluates whether traits evolve toward fixed adaptive optima or track continuously changing optima influenced by other traits.
• Sexual Selection Studies: Investigates relationships between body size, breeding group size, and sexually selected traits.

Methodology:

Blouch fits allometric and adaptive models of continuous trait evolution using Bayesian linear Ornstein-Uhlenbeck models based on fixed or continuous predictors, with Markov chain Monte Carlo sampling for parameter estimation.