Applications of genetic-based estimates of population size are expanding, especially for species where traditional demographic estimation methods are intractable due to the rarity of adult encounters. Estimates of breeding population size (NS ) are particularly amenable to genetic-based approaches as the parameter can be estimated using pedigrees reconstructed from genetic data gathered from discrete juvenile cohorts, thus eliminating the need to sample adults in the population. However, a critical evaluation of how genotyping and sampling effort influence bias in pedigree reconstruction, and how these biases subsequently influence estimates of NS , is needed to evaluate the efficacy of the approach under a range of scenarios. We simulated a model system to understand the interactive effects of genotyping and sampling effort on error in genetic pedigrees reconstructed from the program COLONY. We then evaluated how errors in pedigree reconstruction influenced bias and precision in estimates of NS using three different rarefaction estimators. Results indicated that pedigree error can be minimal when adequate genetic data are available, such as when juvenile sample sizes are large and/or individuals are genotyped at many informative loci. However, even in cases where data are limited, using results of the simulation analysis to understand the magnitude and sources of bias in reconstructed pedigrees can still be informative when estimating NS . We applied results of the simulation analysis to evaluate N ̂ S for a population of Atlantic sturgeon (Acipenser oxyrinchus oxyrinchus) in the Delaware River, USA that is federally endangered in the United States. Our results indicated that NS is likely three orders of magnitude lower than historical breeding population sizes, which is a considerable advancement in our understanding of the population status of Atlantic sturgeon in the Delaware River. Our analyses are broadly applicable in the design and interpretation of studies seeking to estimate NS and can help guide conservation decisions when ecological uncertainty is high. The utility of these results is expected to grow as rapid advances in genetic technologies increase the popularity of genetic population monitoring and estimation.