Constructing a stochastic model of bumblebee flights from experimental data.

Christof Markus Aegerter,Friedrich Lenz,Rainer Klages,Aleksei V Chechkin

doi:10.1371/journal.pone.0059036

Abstract

The movement of organisms is subject to a multitude of influences of widely varying character: from the bio-mechanics of the individual, over the interaction with the complex environment many animals live in, to evolutionary pressure and energy constraints. As the number of factors is large, it is very hard to build comprehensive movement models. Even when movement patterns in simple environments are analysed, the organisms can display very complex behaviours. While for largely undirected motion or long observation times the dynamics can sometimes be described by isotropic random walks, usually the directional persistence due to a preference to move forward has to be accounted for, e.g., by a correlated random walk. In this paper we generalise these descriptions to a model in terms of stochastic differential equations of Langevin type, which we use to analyse experimental search flight data of foraging bumblebees. Using parameter estimates we discuss the differences and similarities to correlated random walks. From simulations we generate artificial bumblebee trajectories which we use as a validation by comparing the generated ones to the experimental data.

Highlights

Foraging Animals The characteristics of the movement of animals play a key role in a variety of ecologically relevant processes, from foraging and group behaviour of animals [1] to dispersal [2,3] and territoriality [4]
Studying the behaviour of animals, simple random walk models have been proven effective in describing irregular paths [5]
While the first studies on random paths of organisms focused on uncorrelated step sequences [6], in many cases of studies of animal behaviour the directional persistence of the animals suggested a modelling in terms of correlated random walks (CRWs) [7,8]

Summary

Introduction

Foraging Animals The characteristics of the movement of animals play a key role in a variety of ecologically relevant processes, from foraging and group behaviour of animals [1] to dispersal [2,3] and territoriality [4]. While the first studies on random paths of organisms focused on uncorrelated step sequences [6], in many cases of studies of animal behaviour the directional persistence of the animals suggested a modelling in terms of correlated random walks (CRWs) [7,8]. In many complex environments an intermittent behaviour of animals is observed. In these cases an animal switches either randomly or in reaction to its environment between different movement patterns. The mechanisms which generate, and the factors which influence this switching behaviour have been shown to be important in understanding and modelling complicated animal paths [9,10,11,12]. With no clear indication of additional intermittency, we will focus on non-intermittent models in the following

Methods

Results

Conclusion