Deep Learning Resolves Representative Movement Patterns in a Marine Predator Species

Chengbin Peng,Carlos M Duarte,Christophe Guinet,Monica Muelbert,Mark A Hindell,Ka-Chun Wong,Daniel P Costa,Robert G Harcourt,Michele Thums,Clive R Mcmahon,Xiangliang Zhang

doi:10.3390/app9142935

Abstract

The analysis of animal movement from telemetry data provides insights into how and why animals move. While traditional approaches to such analysis mostly focus on predicting animal states during movement, we describe an approach that allows us to identify representative movement patterns of different animal groups. To do this, we propose a carefully designed recurrent neural network and combine it with telemetry data for automatic feature extraction and identification of non-predefined representative patterns. In the experiment, we consider a particular marine predator species, the southern elephant seal, as an example. With our approach, we identify that the male seals in our data set share similar movement patterns when they are close to land. We identify this pattern recurring in a number of distant locations, consistent with alternative approaches from previous research.

Highlights

The analysis of animal telemetry data can help researchers identify locations popular to animals, called biological hotspots, and to clarify movement patterns that could improve outcomes of conservation programs protecting vulnerable wildlife [1,2]
Segments with high confidence scores were deemed to be representative ones. We call such segments Representative Segments (RES). They are unique to a specific group, whereas other segments are Common Segments (COS) whose patterns are shared by different animal groups
To investigate the characteristics of RES, we focus our discussion on the segments with top-10% confidence scores obtained by recurrent neural network (RNN)-CM

Summary

Introduction

The analysis of animal telemetry data can help researchers identify locations popular to animals, called biological hotspots, and to clarify movement patterns that could improve outcomes of conservation programs protecting vulnerable wildlife [1,2]. Movement analysis is important for studying animals’ search strategies and their behavioural ecology [3]. Because animals obtain resources (prey, mates, etc.) through movements, their movement patterns reveal important information on species fitness [4]. Given that the study of animal movement through telemetry technologies started more than 30 years ago, there exists a wealth of data on animal movement [5].

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