Testing hypotheses in ecoimmunology using mixed models: disentangling hierarchical correlations.

Area of Science:

• Ecoimmunology
• Evolutionary Biology
• Quantitative Genetics

Background:

• Immune responses exhibit significant variation within and among individuals, populations, and species.
• Understanding this variation is essential for ecological and evolutionary inferences.
• Variation at the individual level links to quantitative genetic parameters.

Purpose of the Study:

• To describe how multi-response mixed-effects models can partition variance in immune responses across hierarchical levels.
• To demonstrate the utility of mixed models for ecoimmunology research.
• To highlight the limitations of conventional statistical approaches.

Main Methods:

• Utilized multi-response mixed-effects models.
• Employed simulated data to test model performance.
• Partitioned variance at within-individual, among-individual, and among-species levels.

Main Results:

• Mixed-effects models successfully partitioned (co)variance across hierarchical levels.
• Simulations showed conventional statistical tools significantly misestimate parameters.
• This highlights potential inaccuracies in existing ecoimmunological research.

Conclusions:

• Multi-response mixed-effects models offer a robust framework for analyzing variation in immune responses.
• Caution is advised when interpreting results from conventional statistical methods in ecoimmunology.
• This modeling approach is vital for advancing ecological and evolutionary insights into immunity.