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

TL;DR

This paper introduces a hidden Markov model-based method to accurately infer and simulate the behavioral segments of central place foraging trips in marine predators, enhancing understanding of their foraging strategies.

Contribution

The study develops a structured 4-state hidden Markov model for analyzing foraging trips, providing a more detailed and realistic inference and simulation framework compared to simpler models.

Findings

The 4-state model captures behavioral sequences more accurately.

Simulations based on the model reflect realistic foraging trip dynamics.

Application to elephant seal data demonstrates model effectiveness.

Abstract

The behaviour of colony-based marine predators is the focus of much research globally. Large telemetry and tracking data sets have been collected for this group of animals, and are accompanied by many theoretical studies of optimal foraging strategies. However, relatively few studies have detailed statistical methods for inferring behaviours in central place foraging trips. In this paper we describe an approach based on hidden Markov models, which splits foraging trips into segments labelled as "outbound", "search", "forage", and "inbound". By structuring the hidden Markov model transition matrix appropriately, the model naturally handles the sequence of behaviours within a foraging trip. Additionally, by structuring the model in this way, we are able to develop realistic simulations from the fitted model. We demonstrate our approach on data from southern elephant seals (Mirounga leonina) tagged on Kerguelen Island in the Southern Ocean. We discuss the differences between our 4-state model and the widely used 2-state model, and the advantages and disadvantages of employing a more complex model.