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 non-invasive DNA sampling can be used to detect close kin.

Genomic databanks and targeted assays help characterise domestic mosquito incursions

“…This knowledge can be useful for building predictive models of pest incursion risk as it can help estimate propagule pressure (Camac, Baumgartner, Hester, Subasinghe, & Collins, 2021), though these estimates will reflect effective rather than census population sizes of incursions. The findings at Tennant Creek pose some additional questions for studies seeking to investigate new invasions using close kin dyad methodologies (Jasper, Hoffmann, & Schmidt, 2021; Waples & Feutry, 2021). While close kin dyad methods have shown promise for investigating invasions of ∼100 generations age (Schmidt et al, 2021), close kin methods should be applied cautiously to invasions that are very new or that are sourced from a small genetic pool.…”

Close kin dyads indicate intergenerational dispersal and barriers

“…aegypti, if the spatial distribution of a full-sib dyad reflects two draws from the mother’s σ ovi kernel, the spatial distribution of a full-first cousin dyad will reflect two draws from the grandmother’s σ ovi kernel and two draws from the parent’s dispersal kernel from egg through to oviposition (i.e. σ 2 1C = 2σ 2 ovi + 2σ 2 PO ) (Jasper et al 2021). Using the additive properties of variance, the difference between the full-sib and full-first cousin dispersal kernels will be equal to two ‘draws’ from the σ PO kernel (i.e.…”

“…However, if first cousins were incorrectly classed as unrelated, this would simply reduce the sample size rather than introduce biases, as the K indisperse method for Ae. aegypti considers only sibs and first cousins (Jasper et al 2021). Considering the uncertainty around estimates, we assigned first cousin dyads conservatively and treated the ‘first cousin’ category as a composite category containing both full and half-first cousins (Jasper et al 2019).…”

“…K indisperse estimates σ from the difference in variance between the spatial distributions of two or more kin categories, such as between full-sibs and full-first cousins. The specific kin categories used depends on the dispersal biology of the species, but individuals must typically be sampled at the same life stage (Jasper et al 2021). In this regard, K indisperse aims to summarise the dispersal variance across a single generation of the spatial pedigree (Bradburd and Ralph 2019), and can be considered a close-kin mark-recapture (CKMR) methodology (Bravington et al 2016).…”

“…Here we do precisely that, and show how our method based on close kin performs favourably at this scale against methods that consider the genetics of the entire population. The same close kin data are also used to estimate σ with K indisperse (Jasper et al 2021), which has previously only been applied to individuals clustered within apartment buildings (Jasper et al 2019). We also use isolation by distance patterns to estimate N w (Rousset 2000).…”