The application of Bayesian hierarchical models to quantify individual diet specialization

Area of Science:

• Ecology
• Evolutionary Biology
• Quantitative Biology

Background:

• Intraspecific variation in ecologically relevant traits is common.
• Individual diet specialization in generalist predators can significantly impact food web dynamics.

Purpose of the Study:

• To compare the accuracy of frequentist maximum likelihood methods with Bayesian hierarchical models for quantifying individual diet specialization.
• To assess the impact of low or variable prey observation numbers on diet specialization estimates.

Main Methods:

• Simulated and empirical data were used to compare two statistical approaches: frequentist maximum likelihood and Bayesian hierarchical models.
• Bayesian models were employed to estimate prey proportions and their variability.

Main Results:

• Frequentist methods, using observed prey proportions, tend to overestimate diet specialization, especially with limited or uneven data per individual.
• Bayesian hierarchical models provide more accurate estimates of diet specialization and its variability.
• Bayesian models allow for uncertainty quantification across different organizational levels (individual, treatment, population).

Conclusions:

• Bayesian hierarchical models offer a superior framework for accurately quantifying individual diet specialization.
• This improved quantification enhances the understanding of intraspecific variation in diet and its ecological consequences.