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Detecting population declines via monitoring the effective number of breeders ( N b )
Author(s) -
Luikart Gordon,
Antao Tiago,
Hand Brian K.,
Muhlfeld Clint C.,
Boyer Matthew C.,
Cosart Ted,
Trethewey Brian,
AlChockhachy Robert,
Waples Robin S.
Publication year - 2021
Publication title -
molecular ecology resources
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.96
H-Index - 136
eISSN - 1755-0998
pISSN - 1755-098X
DOI - 10.1111/1755-0998.13251
Subject(s) - biology , statistics , effective population size , estimator , population , sample size determination , confidence interval , microsatellite , sampling (signal processing) , statistical power , sampling bias , linear regression , regression , evolutionary biology , demography , genetics , genetic variation , mathematics , allele , gene , computer science , filter (signal processing) , sociology , computer vision
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.