Estimating dispersal using close kin dyads: The kindisperse R package

Area of Science:

• Ecology
• Genetics
• Computational Biology

Background:

• Estimating animal dispersal is challenging, especially for small or rare species.
• Genome-wide sequence data offer a novel approach to infer dispersal by identifying close kin dyads.
• Current methods are limited in scope and require further assessment across diverse conditions.

Purpose of the Study:

• To develop an R package and shiny app, kindisperse, for estimating dispersal parameters from close kin distributions.
• To assess the performance of close kin methodologies under various dispersal scenarios and study designs.
• To provide practical guidelines for effective dispersal estimation using genetic data.

Main Methods:

• Development of the kindisperse R package and shiny application.
• Utilizing genome-wide sequence data to detect close kin dyads.
• Simulations to compare close kin methodologies and evaluate study design impacts.

Main Results:

• The kindisperse package enables dispersal parameter estimation from spatial close kin distributions.
• One close kin methodology yielded unbiased dispersal estimates, while another showed downward bias.
• Simulations provided insights into how study design influences dispersal estimation accuracy.

Conclusions:

• kindisperse offers a flexible tool for estimating dispersal across various taxa and life stages.
• The package aids in optimizing study designs for accurate genetic-based dispersal inference.
• Noninvasive DNA sampling with kindisperse is applicable to conservation and pest management.