Inferring the relative importance of navigation and landscape effects for migratory populations using agent-based...

Area of Science:

• Ecology
• Computational Biology
• Animal Behavior

Background:

• Seasonal animal migration is crucial for ecosystems but challenging to predict due to scale and navigation complexity.
• Understanding animal navigation strategies is key to predicting migratory population dynamics.

Purpose of the Study:

• To develop an agent-based model (ABM) simulating migratory animal distributions using true and vector navigation.
• To assess the utility of comparing model outputs with empirical data using monarch butterflies as a case study.

Main Methods:

• An agent-based model (ABM) was created to simulate animal migration with true and vector navigation strategies.
• The model incorporated landscape features like water bodies and elevational gradients.
• Simulated spatial distributions were compared with empirical data from monarch butterfly migrations.

Main Results:

• True navigators were more accurate on homogeneous landscapes, but heterogeneous landscapes facilitated vector navigation.
• Simulations using vector navigation closely matched observed western monarch butterfly overwintering locations.
• The study identified data limitations, such as sampling bias, in empirical observations.

Conclusions:

• Agent-based models (ABMs) are valuable for linking navigation hypotheses to observed migratory patterns.
• Vector navigation across heterogeneous landscapes is a plausible strategy for western monarch butterflies.
• Improved data collection is needed to refine understanding of migratory behaviors.