Revisiting Brownian motion as a description of animal movement: a comparison to experimental movement data

Bearup, Daniel and Benefer, C.M. and Petrovskii, S.V. and Blackshaw, R.P. (2016) Revisiting Brownian motion as a description of animal movement: a comparison to experimental movement data. Methods in Ecology and Evolution, 7 (12). pp. 1525-1537. ISSN 2041-210X. (doi:https://doi.org/10.1111/2041-210X.12615) (Full text available)

Abstract

1) 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. 2) 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. 3) 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. 4) 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.

Item Type:	Article
Additional information:	cited By 3
Subjects:	Q Science > QA Mathematics (inc Computing science) Q Science > QA Mathematics (inc Computing science) > QA276 Mathematical statistics Q Science > QH Natural history > QH541 Ecology
Divisions:	Faculties > Sciences > School of Mathematics Statistics and Actuarial Science Faculties > Sciences > School of Mathematics Statistics and Actuarial Science > Applied Mathematics
Depositing User:	Daniel Bearup
Date Deposited:	30 Nov 2017 12:32 UTC
Last Modified:	08 Jun 2018 09:06 UTC
Resource URI:	https://kar.kent.ac.uk/id/eprint/64311 (The current URI for this page, for reference purposes)
Bearup, Daniel:	https://orcid.org/0000-0001-8524-7659