10.5061/DRYAD.52MB6
Bearup, Daniel
Carl von Ossietzky University of Oldenburg
Benefer, Carly M.
Plymouth University
Petrovskii, Sergei V.
University of Leicester
Blackshaw, Rod P.
Blackshaw Research and Consulting Devon TQ13 0JF UK
Data from: Revisiting Brownian motion as a description of animal movement:
a comparison to experimental movement data
Dryad
dataset
2017
Random walk
diffusion
population flux
trapping
Tenebrio molitor
2017-06-30T00:00:00Z
2017-06-30T00:00:00Z
en
https://doi.org/10.1111/2041-210X.12615
24921 bytes
1
CC0 1.0 Universal (CC0 1.0) Public Domain Dedication
Characterization of patterns of animal movement is a major challenge in
ecology with applications to conservation, biological invasions and pest
monitoring. Brownian random walks, and diffusive flux as their mean field
counterpart, provide one framework in which to consider this problem.
However, it remains subject to debate and controversy. This study presents
a test of the diffusion framework using movement data obtained from
controlled experiments. Walking beetles (Tenebrio molitor) were released
in an open circular arena with a central hole and the number of
individuals falling from the arena edges was monitored over time. These
boundary counts were compared, using curve fitting, to the predictions of
a diffusion model. The diffusion model is solved precisely, without using
numerical simulations. We find that the shape of the curves derived from
the diffusion model is a close match to those found experimentally.
Furthermore, in general, estimates of the total population obtained from
the relevant solution of the diffusion equation are in excellent agreement
with the experimental population. Estimates of the dispersal rate of
individuals depend on how accurately the initial release distribution is
incorporated into the model. We therefore show that diffusive flux is a
very good approximation to the movement of a population of Tenebrio
molitor beetles. As such, we suggest that it is an adequate
theoretical/modelling framework for ecological studies that account for
insect movement, although it can be context specific. An immediate
practical application of this can be found in the interpretation of trap
counts, in particular for the purpose of pest monitoring.
Experimental data and MATLAB codeThis archive contains the experimental
data and MATLAB code used in the publication named. Three folders are
included containing: raw and processed data files; MATLAB functions for
parameter estimation; and MATLAB functions for the generation of power law
random walks. The README file provides additional information about these
folders and the files contained within.supplementary data.zip