Dynamical BLUP modeling of reaction norm evolution, accommodating changing environments, overlapping generations, and...

Area of Science:

• Quantitative genetics
• Evolutionary biology
• Animal breeding

Background:

• Reaction norm evolution is crucial for understanding adaptation in changing environments.
• Existing models like the multivariate breeder's equation and random regression have limitations with field data.
• Multivariate reaction norms are often correlated, complicating independent modeling.

Purpose of the Study:

• To present a novel multivariate linear mixed model for reaction norm evolution.
• To enable estimation of individual reaction norm parameters and their generational updates.
• To disentangle microevolutionary and plasticity components in responses to environmental change, particularly climate change.

Main Methods:

• A multivariate linear mixed model with dynamical incidence and residual covariance matrices.
• A dynamical Best Linear Unbiased Prediction (BLUP) model for estimating individual reaction norm parameters.
• Incorporation of Robertson's secondary theorem of natural selection for updating mean reaction norm parameters across generations.
• Accommodation of overlapping generations and additive genetic relationship matrices.

Main Results:

• The proposed model provides a feasible approach for analyzing field data with reaction norms.
• It allows for the estimation of individual reaction norm parameters at any parent generation.
• The model can update mean reaction norm parameters generationally using Robertson's theorem.
• It enables the separation of microevolutionary and plasticity contributions to climate change responses.

Conclusions:

• The developed multivariate linear mixed model offers a powerful tool for studying reaction norm evolution under environmental change.
• This approach facilitates the analysis of complex genetic and environmental interactions influencing phenotypic plasticity.
• The model's ability to handle field data and disentangle evolutionary components is vital for predicting species' responses to climate change.