Correlated Gaussian random walk models of animal dispersal

TL;DR

This paper introduces a correlated Gaussian random walk model to simulate animal dispersal, analyzing its features, special cases, and numerical simulations, revealing that correlation does not always lead to directional persistence.

Contribution

The paper presents a novel correlated Gaussian random walk model for animal dispersal, including analytical solutions and numerical validation, expanding understanding of dispersal dynamics.

Findings

Analytical solutions for 1D CGRW densities

Numerical simulations agree with theoretical predictions

Correlation does not always produce directional persistence

Abstract

A correlated Gaussian random walk(CGRW) model is proposed as a simple model of animal dispersal. The general features of CGRW is described. We will discuss how from this single model a number of different kinds of correlated random walk can be studied. The CGRWs in one dimension is studied in detail and the special limiting cases are discussed where the probability densities are found analytically. Numerical simulations are performed and the results are found to be in good agreement with the theoretical predictions. Directional persistence in CGRWs is discussed for the one dimensional cases. We will show that correlation does not always give rise to directional persistence.