The effect of the dispersal kernel on isolation-by-distance in a continuous population

TL;DR

This study examines how different dispersal kernels affect isolation-by-distance patterns in populations, confirming that neighborhood size is a key factor and proposing the triangular distribution as an effective null model.

Contribution

The paper demonstrates that various dispersal kernels produce similar isolation-by-distance patterns when neighborhood size is fixed and introduces the triangular distribution as an appropriate null model.

Findings

Dispersal kernels yield similar patterns at fixed neighborhood size.

The triangular distribution is recommended as the null model.

Efficient sampling methods for the triangular distribution are provided.

Abstract

Under models of isolation-by-distance, population structure is determined by the probability of identity-by-descent between pairs of genes according to the geographic distance between them. Well established analytical results indicate that the relationship between geographical and genetic distance depends mostly on the neighborhood size of the population, $N_b = 4{\pi}{\sigma}^2 D_e$, which represents a standardized measure of dispersal. To test this prediction, we model local dispersal of haploid individuals on a two-dimensional torus using four dispersal kernels: Rayleigh, exponential, half-normal and triangular. When neighborhood size is held constant, the distributions produce similar patterns of isolation-by-distance, confirming predictions. Considering this, we propose that the triangular distribution is the appropriate null distribution for isolation-by-distance studies. Under the triangular distribution, dispersal is uniform within an area of $4{\pi}{\sigma}^2$ (i.e. the neighborhood area), which suggests that the common description of neighborhood size as a measure of a local panmictic population is valid for popular families of dispersal distributions. We further show how to draw from the triangular distribution efficiently and argue that it should be utilized in other studies in which computational efficiency is important