Egg and larval collection methods affect spawning adult numbers inferred by pedigree analysis

Analytical methods that incorporate genetic data are increasingly used in monitoring and assessment programs for important rate functions of fish populations (e.g., recruitment). Because gear types vary in efficiencies and effective sampling areas, results from genetic‐based assessments likely differ depending on the sampling gear used to collect genotyped individuals; consequently, management decisions may also be affected by sampling gear. In this study, genetic pedigree analysis conducted on egg and larval Lake Sturgeon Acipenser fulvescens collected from the St. Clair–Detroit River system using three gear types was used to estimate and evaluate gear‐specific differences in the number of spawning adults that produced the eggs and larvae sampled (N_s), the effective number of breeding adults (N_b), and individual reproductive success. Combined across locations and sampling years, pooled estimates were 330 (N_s; point estimate) and 317 (N_b; 95% CI = 271–372). Mean reproductive success was 4.35 with a variance of 5.33 individuals/spawner. Mean ± SE estimated numbers of unique parents per genotyped egg or larva (i.e., adult detection rate) from 2015 samples were 1.140 ± 0.003 for vertically stratified conical nets, 0.836 ± 0.002 for D‐frame nets, and 0.870 ± 0.002 for egg mats. Using samples from 2016, adult detection rates were 0.823 ± 0.001 for D‐frame nets and 0.708 ± 0.001 for egg mat collections. Coancestry values were negatively correlated with adult detection rate. Although genetic pedigree analyses can improve the understanding of recruitment in fish populations, this study demonstrates that estimates from genetic analyses can vary with the targeted life stage (a biologically informative outcome) and sampling methodology. This study also highlights the influence of sampling methods on the interpretation of genetic pedigree analysis results when multiple gear types are used to collect individuals. Development of standardization approaches may facilitate spatial and temporal comparisons of genetic‐based assessment results.