Estimation and simulation of foraging trips in land-based marine predators

Area of Science:

• Marine biology
• Animal behavior
• Quantitative ecology

Background:

• Colony-based marine predators are crucial research subjects globally.
• Extensive telemetry data exists, but statistical methods for inferring central place foraging behaviors are limited.
• Existing studies often focus on track segmentation or optimal foraging theory.

Purpose of the Study:

• To develop and present a statistical approach for inferring behaviors during central place foraging trips.
• To segment foraging trips into distinct behavioral states: outbound, search, forage, and inbound.
• To provide a framework for realistic simulations of foraging behavior.

Main Methods:

• Utilized hidden Markov models (HMMs) to analyze animal movement data.
• Structured the HMM transition matrix to naturally represent the sequence of foraging behaviors.
• Applied the model to telemetry data from southern elephant seals (Mirounga leonina).

Main Results:

• The proposed 4-state HMM successfully segments foraging trips into 'outbound', 'search', 'forage', and 'inbound' phases.
• The model allows for the generation of realistic behavioral simulations.
• Demonstrated the approach using southern elephant seal tracking data from Kerguelen Island.

Conclusions:

• Hidden Markov models offer a robust statistical framework for analyzing complex foraging behaviors in marine predators.
• The 4-state model provides a more detailed understanding of foraging trip structures compared to simpler models.
• This approach enhances the analysis of telemetry data and aids in understanding predator ecology.