Detecting population declines via monitoring the effective number of breeders (Nb). (23rd December 2020)

• Record Type: Journal Article
• Authors: Luikart, Gordon; Antao, Tiago; Hand, Brian K.; Muhlfeld, Clint C.; Boyer, Matthew C.; Cosart, Ted; Trethewey, Brian; Al‐Chockhachy, Robert; Waples, Robin S.
• Is Part Of: Molecular ecology resources. Volume 21:Number 2(2021)
• Page Start: 379
• Page End: 393
• Publication Date: 2020-12-23
• Subjects: computer simulations -- connectivity -- conservation genetics -- effective population size -- genetic monitoring -- population decline -- population fragmentation -- power analysis -- viability; Molecular ecology -- Periodicals; 572.8
• DOI: 10.1111/1755-0998.13251
• Languages: English
• ISSNs: 1755-098X

Abstract

Abstract: Estimating the effective population size and effective number of breeders per year ( N b ) can facilitate early detection of population declines. We used computer simulations to quantify bias and precision of the one‐sample LDNe estimator of N b in age‐structured populations using a range of published species life history types, sample sizes, and DNA markers. N b estimates were biased by ~5%–10% when using SNPs or microsatellites in species ranging from fishes to mosquitoes, frogs, and seaweed. The bias (high or low) was similar for different life history types within a species suggesting that life history variation in populations will not influence N b estimation. Precision was higher for 100 SNPs ( H ≈ 0.30) than for 15 microsatellites ( H ≈ 0.70). Confidence intervals (CIs) were occasionally too narrow, and biased high when N b was small ( N b < 50); however, the magnitude of bias would unlikely influence management decisions. The CIs (from LDNe ) were sufficiently narrow to achieve high statistical power (≥0.80) to reject the null hypothesis that N b = 50 when the true N b = 30 and when sampling 50 individuals and 200 SNPs. Similarly, CIs were sufficiently narrow to reject N b = 500 when the true N b = 400 and when sampling 200 individuals and 5, 000 loci. Finally, we present a linear regression method that provides high power to detect a decline in N b when sampling at least five consecutive cohorts. This study provides guidelines and tools to simulate and estimate N b for age structured populations (https://github.com/popgengui/agestrucnb/ ), which should help biologists develop sensitive monitoring programmes for early detection of changes in N b and population declines.