RDA-PSO: A Computational Method to Quantify the Diffusive Dispersal of Insects

This article introduces a computational method, called Recapture of Diffusive Agents & Particle Swarm Optimization (RDA-PSO), designed to estimate the dispersal parameter of diffusive insects in mark-release-recapture (MRR) field experiments. In addition to describing the method, its properties are discussed, with particular focus on robustness in estimating the observed diffusion coefficient in the presence of uncertainty. It is shown that RDA-PSO provides a simple and reliable approach to quantify insect dispersal that can handle low recapture rates and uneven capture site distributions without the need for area corrections. Tests on synthetic data, for which the actual diffusion coefficient is known, show the method outperforms three techniques based on the solution of the diffusion equation, which are also introduced in this work. Examples of application to real field data for the yellow fever mosquito are provided.

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Publication Details

Journal
Bulletin of Mathematical Biology
Published
2026-01-14
DOI
https://doi.org/10.1007/s11538-025-01577-0
Primary Topic
Species Distribution and Climate Change
Type
article
Field-Weighted Citation Impact
0.00

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article

RDA-PSO: A Computational Method to Quantify the Diffusive Dispersal of Insects

J. Lega, Lidia Mrad
Bulletin of Mathematical Biology
Species Distribution and Climate Change
article

RDA-PSO: A Computational Method to Quantify the Diffusive Dispersal of Insects

J. Lega, Lidia Mrad
article en

Abstract

This article introduces a computational method, called Recapture of Diffusive Agents & Particle Swarm Optimization (RDA-PSO), designed to estimate the dispersal parameter of diffusive insects in mark-release-recapture (MRR) field experiments. In addition to describing the method, its properties are discussed, with particular focus on robustness in estimating the observed diffusion coefficient in the presence of uncertainty. It is shown that RDA-PSO provides a simple and reliable approach to quantify insect dispersal that can handle low recapture rates and uneven capture site distributions without the need for area corrections. Tests on synthetic data, for which the actual diffusion coefficient is known, show the method outperforms three techniques based on the solution of the diffusion equation, which are also introduced in this work. Examples of application to real field data for the yellow fever mosquito are provided.

Bulletin of Mathematical BiologyVol. 88(2)
University of Arizona (US), Mount Holyoke College (US)
University of Arizona
Openalex Percentile: Top 100%
Species Distribution and Climate Change
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