An agent-based model of dialect evolution in killer whales.

Area of Science:

• Marine Biology
• Bioacoustics
• Animal Behavior

Background:

• Killer whales (Orcinus orca) exhibit unique vocal dialects, suggesting complex social learning.
• Understanding the mechanisms behind these dialects is crucial for studying animal communication and cultural evolution.

Purpose of the Study:

• To model killer whale vocal learning and identify rules leading to dialect formation.
• To compare agent-based models with different vocal-learning hypotheses against observed dialect patterns.

Main Methods:

• Developed a new agent-based model simulating killer whale populations.
• Tested ten distinct vocal-learning rules, including genetic transmission, learning from mothers or matrilines, and different variation types (errors, innovations).
• Compared model outputs to patterns of group-specific call repertoires observed in wild killer whales.

Main Results:

• A null model without vocal learning failed to replicate natural dialect patterns.
• Simple learning rules (e.g., mother-only learning with random errors) produced continuous variation, not discrete dialects.
• Models incorporating innovation or divergence from kin, especially in combination, generated more discrete and stable call types resembling wild dialects.
• Different combinations of learning rules can lead to similar dialect patterns.

Conclusions:

• Vocal learning, particularly with innovation and divergence, is essential for killer whale dialect formation.
• The cultural evolution of killer whale dialects is complex and likely arises from multiple interacting learning rules.
• Further research is needed on quantitative features of wild dialects to refine these models and understand cultural transmission.