Variation between individuals and the consequences for diet selection by groups of animals

Area of Science:

• Ecology and Behavioral Biology
• Animal Nutrition and Dietetics
• Mathematical Modeling in Biology

Background:

• Understanding animal diet selection is crucial for ecological studies and wildlife management.
• Individual variation within animal populations significantly influences collective feeding behaviors.
• Predictive models are needed to understand complex diet selection dynamics.

Purpose of the Study:

• To develop and describe a mathematical model for predicting population-level diet selection.
• To incorporate individual animal characteristics (nutrient requirements, food discrimination, information gathering) into the model.
• To explore how population-level responses in diet composition vary with individual parameter distributions.

Main Methods:

• A model was constructed based on three biologically relevant individual parameters: protein requirement, food discrimination ability, and information acquisition.
• Population parameters were modeled using uncorrelated normal distributions, with standard deviation proportional to the mean.
• The model's predictions were tested against six hypothetical experiments with systematic variations in individual parameter means and variations.

Main Results:

• The model successfully predicted diet selection for a population, demonstrating that population responses differ from individual responses.
• Quantitative effects of varying individual parameter means or variations on diet composition and its variability were predictable only through the model.
• The model highlighted the complex interplay between individual traits and population-level feeding strategies.

Conclusions:

• The developed model provides a novel framework for understanding population diet selection based on individual variation.
• Population-level diet selection exhibits emergent properties not apparent at the individual level.
• Further model extensions could enhance its applicability to real-world diet selection challenges.