Learning and Animal Movement

Mark A. Lewis,Jacqueline Frair,Claudius Gros,Susan D. Healy,Jerod A. Merkle,Eliezer Gurarie,William F. Fagan,John M. Fryxell,Marie Auger-Méthé

doi:10.3389/fevo.2021.681704

Abstract

Integrating diverse concepts from animal behavior, movement ecology, and machine learning, we develop an overview of the ecology of learning and animal movement. Learning-based movement is clearly relevant to ecological problems, but the subject is rooted firmly in psychology, including a distinct terminology. We contrast this psychological origin of learning with the task-oriented perspective on learning that has emerged from the field of machine learning. We review conceptual frameworks that characterize the role of learning in movement, discuss emerging trends, and summarize recent developments in the analysis of movement data. We also discuss the relative advantages of different modeling approaches for exploring the learning-movement interface. We explore in depth how individual and social modalities of learning can matter to the ecology of animal movement, and highlight how diverse kinds of field studies, ranging from translocation efforts to manipulative experiments, can provide critical insight into the learning process in animal movement.

Highlights

Animal movement, in the form of translocation from one locale to another, takes many forms and is critical to ecological processes
Employing two related definitions of learning, one from psychology and the other related to computer science, we evaluate the benefits, costs and limitations of learning in the context of animal movement
We develop links to related disciplines: psychology, animal cognition, and machine learning

Summary

INTRODUCTION

In the form of translocation from one locale to another, takes many forms and is critical to ecological processes. Agent-based models of foragers with spatial memory have shown how fitness accrues from moving to acquire reliable information, even when that movement samples sub-optimal patches (Bracis et al, 2015). This is clear when naïve animals are presented with an unfamiliar environment and movement is exploratory. Even experienced individuals can benefit by spatially sampling a dynamic environment, in particular when resources can be depleted (Boyer and Walsh, 2010) or predation risk can change (Bracis et al, 2018) In this case, movement keeps current the information needed for appropriate decision making.

Shortstopping

Vertical transmission

Memory of past patch quality

CONCLUSION AND NEW HORIZONS