Estimating the number of contributors to DNA mixtures provides a novel tool for ecology. Issue 1 (11th September 2018)

• Record Type: Journal Article
• Authors: Sethi, Suresh A.; Larson, Wesley; Turnquist, Keith; Isermann, Dan
• Editors: Kembel, Steven
• Is Part Of: Methods in ecology and evolution. Volume 10:Issue 1(2019)
• Page Start: 109
• Page End: 119
• Publication Date: 2018-09-11
• Subjects: aquatic ecosystems -- DNA stain -- eDNA -- fisheries -- population genetics -- spatial ecology; Ecology -- Periodicals; Evolution -- Periodicals; 577
• DOI: 10.1111/2041-210X.13079
• Languages: English
• ISSNs: 2041-210X

Abstract

Abstract: Mixtures of DNA from multiple contributors present a novel opportunity to count individuals to inform fish and wildlife ecology. We apply a likelihood‐based framework to estimate the number of contributors to a DNA mixture for ecological applications. We then assess the performance of DNA mixture estimation through a combination of simulation analyses, laboratory testing, and a field trial to estimate fish predation rates from stomach content analysis. Simulations indicated reasonably sized genetic marker panels could estimate the number of contributors to mixtures comprised of up to 10 individuals, with potential to resolve larger mixtures with additional markers. Mixture estimates demonstrated robustness to common genotyping errors associated with fish and wildlife genetics applications. Laboratory trials demonstrated that DNA combined from multiple yellow perch ( Perca flavescens ) could be successfully genotyped with a 14‐loci microsatellite panel and led to successful estimation for up to 5‐contributor mixtures. Stomach content analysis with DNA mixtures indicated a 5‐fold increase in estimated predation rates of yellow perch by largemouth bass ( Micropterus salmoides ) relative to conventional visual assessment of diet contents which can miss partially digested prey items. DNA mixtures have potential to expand applications of count‐based ecological analyses. Technical challenges in generating genotypes from DNA mixtures may initially limit their use, however, advances in next generation genotyping platforms are anticipated to surmount these obstacles. Chiefly, we envision opportunity for DNA mixtures to advance eDNA analysis beyond presence/absence based inference to enumeration of specimens.