Estimating dispersal using close kin dyads: The kindisperse R package

Abstract

Investigating dispersal in animal populations can be difficult, particularly for taxa that are hard to directly observe such as those that are small or rare. A promising solution may come from new approaches that use genome-wide sequence data to detect close kin dyads and estimate dispersal parameters from the distribution of these dyads. These methods have so far only been applied to mosquito populations. However, they should have broad applicability to a range of taxa, although no assessment has yet been made on their performance under different dispersal conditions and study designs. Here we develop an R package and shiny app, kindisperse, that can be used to estimate dispersal parameters from the spatial distribution of close kin. kindisperse can handle study designs that target different life stages and allows for a range of dispersal kernel shapes and organismal life histories; we provide implementation examples for a vertebrate (Antechinus) and an invertebrate (Aedes). We use simulations run in kindisperse to compare the performance of two published close kin methodologies, showing that one method produces unbiased estimates whereas the other produces downward-biased estimates. We also use kindisperse simulations to investigate how study design affects dispersal estimates, and we provide guidelines for the size and shape of sample sites as well as the number of close kin needed for accurate estimates. kindisperse is easily adaptable for application to a variety of research contexts ranging from invasive pests to threatened species where noninvasive DNA sampling can be used to detect close kin.