A random walk model that accounts for space occupation and movements of a large herbivore

Geoffroy Berthelot,Vincent Bansaye,Sonia Saïd

doi:10.1038/s41598-021-93387-2

Abstract

Animal movement has been identified as a key feature in understanding animal behavior, distribution and habitat use and foraging strategies among others. Large datasets of invididual locations often remain unused or used only in part due to the lack of practical models that can directly infer the desired features from raw GPS locations and the complexity of existing approaches. Some of them being disputed for their lack of biological justifications in their design. We propose a simple model of individual movement with explicit parameters, based on a two-dimensional biased and correlated random walk with three forces related to advection (correlation), attraction (bias) and immobility of the animal. These forces can be directly estimated using individual data. We demonstrate the approach by using GPS data of 5 red deer with a high frequency sampling. The results show that a simple random walk template can account for the spatial complexity of wild animals. The practical design of the model is also verified for detecting spatial feature abnormalities and for providing estimates of density and abundance of wild animals. Integrating even more additional features of animal movement, such as individuals’ interactions or environmental repellents, could help to better understand the spatial behavior of wild animals.

Highlights

Animal movement has been identified as a key feature in understanding animal behavior, distribution and habitat use and foraging strategies among others
The modeling of animal movement includes a wide range of methodologies: biased and/or correlated random walks (BCR)[8,9,10,11], the disputed Lévy Flight/walk[12,13,14,15,16], Stochastic Differential Equation (SDE)[17,18,19,20] including diffusion models based on the two-dimensional Ornstein-Uhlenbeck p rocess[21,22,23,24,25], Hidden Markov Models (HMMs)[26,27,28], state space m odels[29], step-selection functions[30] and other more exotic algorithms using ad-hoc rules to mimic movement features such as memory[31,32]
Some key features of animal movement have already been identified by previous studies, including diffusion which corresponds to an isotropic random motion, where the individual has the same probability to go in all directions; Attraction where the movement of the animal is anisotropic and is confined in an area or domain, according to36 and other s tudies[3], while the attraction may depend on the distance from the isobarycenter of locations[37]; Inertia where the movement of the animal is shaped by foraging tasks where the animal alternates exploration periods—the path has high tortuosity—with straightforward movements[38]

Summary

Introduction

Animal movement has been identified as a key feature in understanding animal behavior, distribution and habitat use and foraging strategies among others. Some key features of animal movement have already been identified by previous studies, including diffusion (or randomness) which corresponds to an isotropic random motion, where the individual has the same probability to go in all directions; Attraction (directional bias) where the movement of the animal is anisotropic and is confined in an area or domain, according to36 and other s tudies[3], while the attraction may depend on the distance from the isobarycenter of locations[37]; Inertia (correlated component) where the movement of the animal is shaped by foraging tasks where the animal alternates exploration periods—the path has high tortuosity—with straightforward movements[38] These three features can be implemented as parameters of a BCR

Objectives

Methods

Results

Conclusion